data engineer

data scientist

senior data scientist

ai engineer

senior ai engineer

lead data architect

Hiscox

<div>
 
 Job Type
 
 
 
 Permanent
 
 
 
 Build a brilliant future with Hiscox
 
 
 
 
 Reporting to: Re &amp; ILS Head of Data
 
 
 
 
 Location:
 
 London
 
 
 Type: Permanent
 
 
 
 Role Overview
 
 
 
 The Lead Data Scientist is a crucial member of the
 
 extended
 
 RE&amp;ILS Tech Leadership Team, responsible for building, leading, and nurturing a highly capable (New)
 
 Data Science and Machine Learning
 
 chapter.
 
 
 This role facilitates the successful delivery of new AI Cloud-based solutions against our growing list of business use cases (involving complex Data Analytics, Machine Learning, and AI Agent based solutions).
 
 
 You will co-lead the new Data Science chapter with the RE&amp;ILS Head of Data and help ensure its members provide Data Science capabilities and insights to support decision-making across RE&amp;ILS strategic Value Streams. Whilst the chapter grows, you are expected to be
 
 hands-on
 
 actively helping to architect, design, and build AI or Data Science-based solutions, using internally trained models, LLMs and/or a combination of both.
 
 
 Whilst undertaking this pioneering work it is essential that you and your entire chapter operates within the boundaries of the
 
 Group AI Governance Framework
 
 and maintains rigorous data governance standards. You will be accountable of this adherence.
 
 
 This is likely to be your first managerial role and the first time you have had one or more direct reports &ndash; having previously been a Senior Data Scientist or Machine Learning Engineer yourself. In this role you will be responsible for the management of a 1 to 4 junior and mid-level Hiscox FTEs and Partner resources too. You will be expected to support the Head of Data with attracting and retaining the best talent; helping to manage their career progression and ensure they are highly engaged, find the work fulfilling, and have the ability to rapidly deliver value to the business. As this is likely to be your first managerial role you will be supported and guided by other Data Leaders &ndash; Head of Data and the Hiscox Group Head of Data Science as well.
 
 
 
 About Hiscox Re &amp; ILS
 
 
 
 Hiscox Re &amp; ILS is located in
 
 Bermuda and London
 
 and underwrites reinsurance risks from around the world on behalf of our own and other people&rsquo;s capital. The team of over 100 smart, enthusiastic individuals are committed to providing top quality service to brokers and clients across a broad range of reinsurance products. Our approach is simple; match the best underwriting risk against globally diversified capital. Our proactive, product-led underwriting approach is complemented by a commitment to world class service and purpose-built analytics and systems. We know our people are our biggest asset so finding and retaining the best talent is critical to achieving our vision.
 
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Chapter Leadership &amp; Talent Management
 </li>
 <li>
 People Management: Lead, recruit, mentor, coach, train, and retain a high-performing chapter of Data Scientists and Machine Learning Engineers (expected to grow to over the next few years), including both Hiscox FTE and Partner colleagues. Inspire chapter members to be curious, embrace new ideas, and nurture psychological safety to help build a high-performance culture.
 </li>
 <li>
 In this role you will manage at least one to two Hiscox data scientists and some partner resources too. You will be expected to manage their performance with relevant PDRs and help to calibrate their overall performance ratings with other people managers in the team. You will work with the Head of Data and Group Head of Data Science to help tailor the training needs to suit each individual and ensure they have the opportunity to grow and progress in their chosen career paths.
 </li>
 <li>
 Culture &amp; Development: Cultivate a collaborative, engaged, and fulfilled team culture. Provide technical leadership, career guidance, and direction to the team, ensuring successful delegation to all chapter members.
 </li>
 <li>
 Best Practice: Ensure the chapter adheres to best practices in data science, AI, and complex analytics, sharing knowledge across the wider Hiscox Analytics and Data community through active participation in Communities of Practice.
 </li>
 <li>
 Mentoring: Mentor and educate others in the various data science techniques, encouraging the team to continually learn &ndash; especially supporting the adoption of LLMs and proprietary models that could significantly accelerate our ability to deploy value.
 </li>
 <li>
 Solution Delivery &amp; ML Ops Excellence
 </li>
 <li>
 Hands-On Architecture: Get hands-on to help architect, design, and build complex models that integrate into existing IT solutions to improve data-driven decision-making throughout the Reinsurance Value Chain. Ensure you and your chapter members work closely with Platform Engineers and Architects to create the most optimal and safe solutions.
 </li>
 <li>
 ML Ops Platform: Actively work with the Head of Platform Engineering to establish and maintain a professional ML Ops platform to facilitate the effective support of developed models in production. This includes embedding a rigorous model evaluation process, ensuring consistent model refresh/rebuild capabilities due to model or data drift, and implementing effective monitoring and alerts against rogue behaviour.
 </li>
 <li>
 Speed of Execution: Empower the chapter to work effectively within autonomous squads, delivering desired and valuable outcomes in an incremental manner while correctly balancing accuracy, fast return on investment, and reliability. Understand when to move on and fail fast &ndash; as a lot of AI use cases will be highly experimental in nature with no guarantees of success.
 </li>
 <li>
 Pipeline Collaboration: Work with Data Engineering and other teams to properly understand and contribute to the requirements and building of necessary data pipelines/products consumed by our models.
 </li>
 <li>
 Strategy, Governance &amp; Innovation
 </li>
 <li>
 Use Case Identification: Continually work with key business leaders, the Head of Data and key stakeholders to identify new use cases and opportunities where data science/AI can deliver a competitive advantage or reduce waste/costs through efficiency gains.
 </li>
 <li>
 Governance &amp; Compliance: Champion data and model governance, working within the Group AI Governance Framework. Ensure a mature understanding of data governance, lineage, and version control, and enforce data privacy by design in all new work.
 </li>
 <li>
 Innovation: Maintain a strong awareness of new trends and techniques in Data Science, AI, and Machine Learning. Drive the identification of innovative data solutions and technologies, and actively undertake Proof of Concepts and R&amp;D to deliver reliable insights and potential next steps quickly.
 </li>
 <li>
 Communication: Ensure clear and effective communication of data science solutions and outcomes across business layers, technical teams, and senior leadership.
 
 
 
 </li>
 </ul>
 
 Required Experience And Structure
 
 
 
 <ul>
 <li>
 Reporting: Reports into the RE&amp;ILS Head of Data with a dotted line into the new Group Head of Data Science.
 </li>
 <li>
 Direct Reports: Direct line management for Data Scientists and Machine Learning Engineers within the RE&amp;ILS Data Science Chapter.
 </li>
 <li>
 Key Partners: RE&amp;ILS CTO, Group Head of Data Science, RE&amp;ILS Head of Data, Product Managers, Product Owners, and strategic partners (e.g. Google Cloud, Microsoft).
 
 
 
 </li>
 </ul>
 
 Candidate Profile
 
 
 
 The successful candidate will be an energetic, highly capable, and passionate Senior Data Scientist who wants to move into their first people management role. You will need to balance technical expertise with effective people management and strategic business partnership. You must be comfortable working in an FCA-regulated business, advocating for a data-driven approach, and driving innovation while maintaining rigorous AI governance standards. You will have a strong determination and resilience and not fear failure or the inevitable setbacks often found in highly experimental work.
 
 
 
 Main Experience Required Is
 
 
 
 <ul>
 <li>
 Deep Interest in Leadership: As an experienced data scientist or machine learning engineer, has previously demonstrated excellent traits in mentoring and coaching more junior team members and has been happy to invest in them - clearly demonstrating they care about other peoples&rsquo; career progression. Also you can evidence some successful business outcomes that you personally drove forward using advanced Data Science/AI &ndash; as this most likely is your first leadership role.
 </li>
 <li>
 Technical Depth: Extensive "hands-on" experience in architecting, designing, and building complex ML models, with a strong understanding of cloud services and the AI/ML/LLM ecosystem.
 </li>
 <li>
 ML Ops Maturity: Proven ability to build, maintain, and support production-deployed models, including establishing effective monitoring, evaluation, and refresh processes.
 </li>
 <li>
 AI and Data Governance: Mature understanding of AI and Data governance - data obfuscation &amp; data privacy laws, data version control, data lineage, and the application of AI governance frameworks in a regulated environment.
 </li>
 <li>
 Stakeholder Management: Experience in managing and driving collaboration with business unit leaders (Underwriters, pricing teams, MI team) and external technology partners like Google Cloud, Snowflake, DataIku and Microsoft.
 </li>
 <li>
 Education: Bachelor's/Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering) or equivalent.
 </li>
 <li>
 Capacity for independent, high-quality research and exploratory data analysis, with the ability to develop and apply novel methodologies to solve ill-defined and challenging business problems.
 </li>
 <li>
 Exceptional proficiency in Python (and R) for data science, coupled with SQL capabilities for data manipulation and extraction. Demonstrable mastery in a wide range of machine learning and statistical modelling techniques, from classical linear models and tree-based methods to advanced deep learning architectures.
 </li>
 <li>
 Real world experience with, or demonstrable capability for, applications of Generative AI, Large Language Models (LLMs), AI Agents and related areas such as Natural Language Processing (NLP) or Computer Vision, relevant to business solutions.
 </li>
 <li>
 Demonstrate a Solid understanding of foundational statistics and experimental design. Pioneer the development and robust deployment of highly impactful machine learning and AI-driven solutions, directly contributing to significant commercial value, quantifiable business growth, or efficiency gains across the organization.
 </li>
 <li>
 Ability to translate complex data science insights into tangible, measurable improvements across critical business processes and key performance indicators, ensuring the real-world adoption and beneficial outcomes of our analytical work.
 </li>
 <li>
 Ability to elevate the technical maturity and capabilities of the data science chapter by shaping and implementing industry-leading AI, data, and analytics techniques, methodologies, and engineering best practices.
 </li>
 <li>
 Educator: shown the ability to train and coach more junior and mid-level team members - ensuring they have excellent career progression and become high performers.
 
 
 
 </li>
 </ul>
 If you are a visionary expert looking for your first leadership role and ready to accelerate our value delivery and champion all things Data Science and AI, we invite you to apply for the position of Lead Data Scientist at Hiscox Re &amp; ILS. Join our team and be a catalyst for AI adoption in a dynamic, forward-thinking organization.
 
 
 
 Work with amazing people and be part of a unique culture
 
</div>

Lead Data Scientist

Faculty

<div><h2 class="jobSectionHeader">Why Faculty? 
</h2> 
We established Faculty in 2014 because we thought that AI would be the most important technology of our time. Since then, we&rsquo;ve worked with over 350 global customers to transform their performance through human-centric AI. You can read about our real-world impact here.
We don&rsquo;t chase hype cycles. We innovate, build and deploy responsible AI which moves the needle - and we know a thing or two about doing it well. We bring an unparalleled depth of technical, product and delivery expertise to our clients who span government, finance, retail, energy, life sciences and defence.
Our business, and reputation, is growing fast and we&rsquo;re always on the lookout for individuals who share our intellectual curiosity and desire to build a positive legacy through technology.
AI is an epoch-defining technology, join a company where you&rsquo;ll be empowered to envision its most powerful applications, and to make them happen.<div> 
</div><h2 class="jobSectionHeader">
About the team</h2><div> 
</div>Our Public Services Business Unit is committed to leveraging AI for the benefit of individual citizens and the public good. 
 
From our work informing strategic government decisions, to optimising our NHS, through to reducing bureaucratic backlogs - we know that AI offers opportunities to drive improvements at every level of Government and we are proud to lead on some of the most impactful work happening in the sector. 
 
Because of the nature of the work we do with our Government clients, you may need to be eligible for UK Security Clearance (SC) and willing to work on site with these clients from time to time.<h2 class="jobSectionHeader">
About the role</h2><div></div> 
As a Lead Data Scientist, you'll take on a pivotal, entrepreneurial role, functioning as the technical expert who thrives on complexity and commercial impact. You will be responsible for setting the technical direction and ensuring the high-quality, scalable delivery of our most challenging, high-impact projects. 
 
 
 
This position combines deep expertise in machine learning with strategic oversight to define project roadmaps, manage technical risk, and architect reliable solutions. Your focus will be on driving innovation, mentoring cross-functional teams, and actively shaping both our technical standards and long-term customer relationships.<div></div><h2 class="jobSectionHeader"> 

What you'll be doing:</h2><div></div><ul><li> 
Setting the technical direction for complex, business-critical projects and expertly balancing trade-offs between speed, innovation, and reliability.</li><li>
Designing and implementing reliable, production-grade technical solutions, ensuring comprehensive documentation of architectures and specifications.</li><li>
Defining project problems, developing clear roadmaps, and overseeing end-to-end delivery across multi-disciplinary workstreams.</li><li>
Leading technical scoping and feasibility studies for high-value sales opportunities and strategic customer engagements.</li><li>
Managing relationships and communications with demanding clients, fostering trust and aligning technical solutions with shared long-term commercial goals.</li><li>
Driving the adoption of best practices, shared resources, and robust technical processes across the wider Data Science craft.</li><li>
Mentoring and developing other data scientists and team members, actively contributing to the growth and technical excellence of the organisation.</li></ul><div> 
</div><h2 class="jobSectionHeader">
Who we're looking for:</h2><div></div><ul><li> 
You bring depth of expertise in at least one machine learning domain and strong technical breadth across the entire data science landscape.</li><li>
You are a skilled technical leader, proficient in mentoring individuals, managing teams (including other managers), and rolling out impactful tools and workflows.</li><li>
You have proven project management expertise, capable of dividing complex, ill-defined problems into actionable, clearly defined workstreams with timelines you can defend.</li><li>
You are adept at managing ill-defined, high-risk tasks, consistently delivering innovative and practical outcomes under commercial pressure.</li><li>
You possess strong customer leadership skills, able to act as a trusted technical advisor and drive long-term strategic relationships with demanding clients.</li><li>
You excel at cross-functional collaboration, effectively aligning technical strategy with Engineering, Commercial (BD), and Infrastructure teams.</li><li>
You have experience extending technical oversight to business unit-level initiatives, using your vision to influence and contribute to organisational success.</li></ul><div> 
</div><h2 class="jobSectionHeader">
Our Interview Process</h2><div></div><ul><li> 
Talent Team Screen (30 minutes)</li><li>
Introduction to the team (45 minutes)</li><li>
Technical Interview (90 minutes)
</li><li>Commercial Interview (60 minutes)</li></ul>
#LI-PRIO<h2 class="jobSectionHeader">
Our Recruitment Ethos</h2>
We aim to grow the best team - not the most similar one. We know that diversity of individuals fosters diversity of thought, and that strengthens our principle of seeking truth. And we know from experience that diverse teams deliver better work, relevant to the world in which we live. We&rsquo;re united by a deep intellectual curiosity and desire to use our abilities for measurable positive impact. We strongly encourage applications from people of all backgrounds, ethnicities, genders, religions and sexual orientations.
Some of our standout benefits:<ul><li>
Unlimited Annual Leave Policy</li><li>
Private healthcare and dental</li><li>
Enhanced parental leave</li><li>
Family-Friendly Flexibility &amp; Flexible working</li><li>
Sanctus Coaching</li><li>
Hybrid Working</li></ul>
If you don&rsquo;t feel you meet all the requirements, but are excited by the role and know you bring some key strengths, please don't hesitate in applying as you might be right for this role, or other roles. We are open to conversations about part-time hours.</div>

TPXimpact

<div><h3 class="jobSectionHeader">About The Role</h3>

Job level: 10 
 
We&rsquo;re looking for a Lead Data Engineer to join our Data Engineering and Analytics practice. 

In this role, you will: 
<div></div>
<ul>
<li>Lead the design, development, management and optimisation of data pipelines to ensure efficient data flows, recognising and sharing opportunities to reuse data flows where possible.
</li><li>Coordinate teams and set best practices and standards when it comes to data engineering principles.
</li><li>Champion data engineering across projects and clients. 
</li></ul><div></div><div>Responsibilities
<ul>
<li>Lead by example, holding responsibilities for team culture, and how projects deliver the most impact and value to our clients.
</li><li>Be accountable for the strategic direction, delivery and growth of our work.
</li><li>Lead teams, strands of work and outcomes, owning commercial responsibilities.
</li><li>Hold and manage uncertainty and ambiguity on behalf of clients and our teams.
</li><li>Ensure teams and projects are inclusive through how you lead and manage others.
</li><li>Effectively own and hold the story of our work, ensuring we measure progress against client goals and our DT missions.
</li><li>Work with our teams to influence and own how we deliver more value to clients, working with time and budget constraints.
</li><li>Strategically plan the overall project and apply methods and approaches.
</li><li>Demonstrably share work with wider audiences.
</li><li>Elevate ideas through how you write, speak and present. 
</li></ul><div><div>Dimensions</div> 
<div>
</div><div><ul><li>Headcount: Typically leads a multidisciplinary team or multiple workstreams (team size 5&ndash;15)
</li></ul><ul><li>Resource complexity: Provides leadership across multiple workstreams or technical domains within a project or programme. Responsible for delivery coordination, prioritisation, and quality, often overseeing more junior leads or specialists.
</li><li>Problem-solving responsibility: Solves highly complex problems, balancing technical, user, business, and operational needs. Applies expert judgement to make decisions, manage risks, and guide teams through ambiguity.
</li><li>Change management requirements: Leads or co-leads significant change initiatives. Responsible for managing stakeholder expectations, supporting adoption, and embedding sustainable ways of working.
</li><li>Internal/External interactions: Acts as a trusted partner to client and internal stakeholders at multiple levels. Leads workshops, presentations, and stakeholder engagement to ensure buy-in, alignment, and delivery clarity.
</li><li>Strategic timeframe working towards: Works across mid- to long-term delivery cycles (6&ndash;12 months), ensuring that near-term work supports broader programme and client objectives.
</li></ul></div></div></div><h3 class="jobSectionHeader">About You</h3>
Professional knowledge and experience

Essential
<ul>
<li>Proven experience in data engineering, data integration and data modelling
</li><li>Expertise with cloud platforms (e.g. AWS, Azure, GCP)
</li><li>Expertise with modern cloud data platforms (e.g. Microsoft Fabric, Databricks)
</li><li>Expertise with multiple data analytics tools (e.g. Power BI)
</li><li>Deep understanding of data warehousing concepts, ETL/ELT pipelines and dimensional modelling
</li><li>Proficiency in advanced programming languages (Python/PySpark, SQL)
</li><li>Experience in data pipeline orchestration (e.g. Airflow, Data Factory)
</li><li>Familiarity with DevOps and CI/CD practices (Git, Azure DevOps etc)
</li><li>Ability to communicate technical concepts to both technical and non-technical audiences
</li><li>Proven experience in delivery of complex projects in a fast paced environment with tight deadlines 
</li></ul> 
Desirable
<ul>
<li>Advanced knowledge of data governance, data standards and best practices.
</li><li>Experience in a consultancy environment, demonstrating flexibility and adaptability to client needs.
</li><li>Experience defining and enforcing data engineering standards, patterns, and reusable frameworks
</li><li>Professional certifications in relevant technologies (e.g. Microsoft Azure Data Engineer, AWS Data Analytics, Databricks Certified Professional Data Engineer) 
</li></ul><div></div><div>Skills

Data Development Process
<ul>
<li>Design, build and test data products that are complex or large scale</li>
<li>Build and lead teams to complete data integration services integration and reusable pipelines that meet performance, quality and scalability standards</li>
<li>Collaborate with architects to align solutions with enterprise data strategy and target architectures</li>
</ul>
Data Engineering and Manipulation
<ul>
<li>Work with data analysts, engineers and data science and AI specialists to design and deliver products into the organisation effectively.</li>
<li>Understand the reasons for cleansing and preparing data before including it in data products and can put reusable processes and checks in place.</li>
<li>Access and use a range of architectures (including cloud and on-premise) and data manipulation and transformation tools deployed within the organisation.</li>
<li>Optimise data pipelines and queries for performance and cost efficiency in distributed environments</li>
</ul>
Testing (Data)
<ul>
<li>Review requirements and specifications, and define system integration testing conditions for complex data products and support others to do the same</li>
<li>Identify and manage issues and risks associated with complex data products and support others to do the same</li>
<li>Analyse and report system test activities and results for complex data products and support others to do the same</li>
</ul>
Other Skills
<ul>
<li>Proficiency in developing and maintaining complex data models (conceptual, logical and physical).</li>
<li>Strong skills in data governance and metadata management.</li>
<li>Experience with data integration design and implementation.</li>
<li>Ability to write efficient, maintainable code for large scale data systems.</li>
<li>Experience with CI/CD pipelines, version control, and infrastructure-as-code (e.g. Git, Azure DevOps).</li>
<li>Strong stakeholder communication skills, with the ability to translate technical concepts into business terms.</li>
<li>Ability to mentor junior engineers, foster collaboration, and build a high-performing data engineering culture.</li>
</ul><div>Behaviours and PACT values
<ul><li>Purpose: Be values-driven, recognising that our client's needs are paramount. Approach client engagements with professionalism and creativity, balancing commercial and operational needs.</li>
<li>Accountability: Be accountable for delivering your part of a project on time and under budget and working well with other leaders. Lead by example, promoting a culture where quality and client experience are foremost.</li>
<li>Craft: Balance multiple priorities while leading high-performing teams. Navigate ambiguity and set the technical direction and approach to support positive outcomes.</li>
<li>Togetherness: Collaborate effectively with others across TPXimpact. Build strong relationships with colleagues and clients.</li></ul><div> 
</div>
 
</div></div><h3 class="jobSectionHeader">About Us</h3>
<div>People-Powered Transformation</div> 
<div></div>
<div>
We're a purpose driven organisation, supporting organisations to build a better future for people, places and the planet. Combining vast experience in the public, private and third sectors and expertise in human-centred design, data, experience and technology, we&rsquo;re creating sustainable solutions ready for an ever-evolving world. 


At the heart of TPXimpact, we&rsquo;re collaborative and empathetic. We&rsquo;re a team of passionate people who care deeply about the work we do and the impact we have in the world. We know that change happens through people, with people and for people. That&rsquo;s why we believe in people-powered transformation. 


Working in close collaboration with our clients, we seek to understand their unique challenges, questioning assumptions and building in their teams the capabilities and confidence to continue learning, iterating and adapting. 


Benefits Include:
<ul>
<li>30 days holiday + bank holidays</li>
<li>2 volunteer days for causes that you are passionate about</li>
<li>Maternity/paternity - 6 months Maternity Leave, 3 months Paternity Leave</li>
<li>Life assurance</li>
<li>Employer pension contribution of 5%</li>
<li>Health cash plan</li>
<li>Personal learning and development budget</li>
<li>Employee Assistance Programme</li>
<li>Green incentive programmes including Electric Vehicle Leasing and the Cycle to Work Scheme</li>
<li>Health assessments</li>
</ul>
</div><div>About TPXimpact - Digital Transformation 

We drive fundamental change in approaches to product and service development, delivery and technology. Our agile, multidisciplinary teams use technology, design and data to deliver better results, improving outcomes for individuals, organisations and communities. 
By working in the open, in partnership with our clients, we not only transform their systems and services but also build the capability of their teams, so work can continue without us in the longer term. Our focus is sustainable change, always delivered with positive impact. 


We&rsquo;re an inclusive employer, and we care about diversity in our teams. Let us know in your application if you have accessibility requirements during the interview.</div></div>

Lead Data Engineer

Greyparrot

<div>
 
 About The Role
 
 
 
 The world is in a waste crisis. Currently we produce 2.1 billion tons of solid waste per year. Data collection of the waste we produce is non-existent, meaning no systematic transparency and no accountability. It means that recycling targets are not upheld, dumping of waste into our oceans remains nobody&rsquo;s responsibility, recyclables get sent to landfill or incineration, and producers have little visibility of how their packaging performs at end of life. Recycling rates remain at 10% and, unless we change, by 2040 the plastic stock in the ocean will have quadrupled - a problem that already costs society $1.5 trillion each year.
 
 
 Our mission is to increase transparency and automation in waste management to accelerate the circular economy. Greyparrot's camera and AI systems generate granular, real-time waste composition data across facilities worldwide. That data is only valuable if it can be turned into insight that clients trust and act on.
 
 
 We are looking for a Lead Data Scientist to act as the &ldquo;engine room&rdquo; of our data methodology, transforming raw computer vision outputs into insights the industry can rely on.
 
 
 The near-term priority is delivery; owning the contracted Deepnest client analytics, the reports and data clients have paid for, shipped on time and to a high standard. The metric engine work is what makes delivery scalable and trusted; developing the statistical methodology that turns raw computer vision outputs into defensible, quantified waste metrics. Both matter from day one.
 
 
 Longer term will support other customer &amp; marketing facing work as well.
 
 
 You will report directly to the CTO and have one direct report, Data Analyst, with scope to grow as the business scales. You will sit alongside the Head of Data R&amp;D who owns longer-term structural research upstream of the metric engine.
 
 
 The data is physical-world data: noisy, incomplete, and derived from computer vision models running in live recycling facilities. This is not a clean-warehouse role. It suits someone who treats messy data as the problem to solve, not a reason to wait.
 
 
 This role will ideally be based in our London office at least one day a week, and reports to our CTO.
 
 
 
 Outcomes
 
 
 
 These are the results you will be held to. How you achieve them is yours to figure out.
 
 
 <ul>
 <li>
 The metric engine advances The next iteration of Greyparrot's statistical modelling framework is implemented; strengthening how we reconcile process flows, extrapolate across coverage gaps, and quantify confidence in outputs. At 12 months, a credible path toward a confidence-aware, probabilistic foundation is underway. The methodology is documented, defensible, and ready to be productionised by ML Ops.
 </li>
 <li>
 Contracted deliverables ship on time, every time Deepnest clients receive their analytical reports and insight outputs to a consistently high standard and on schedule. The methodology behind the numbers is defensible, the findings are actionable, and clients trust what they receive. There are no surprises at delivery.
 </li>
 <li>
 Insight delivery is repeatable, not heroic A documented framework - templates, quality standards, methodology - exists so output quality does not depend on starting from scratch each engagement. The process is written down, transferable, and does not live in your head.
 </li>
 <li>
 R&amp;D and delivery are in sync The Head of Data, R&amp;D has a clear, consistent picture of which model outputs translate to client value. You provide that feedback loop reliably, and it shapes what gets prioritised on the research roadmap. There is no gap between what the models produce and what clients actually need.
 
 
 
 </li>
 </ul>
 
 Experience &amp; Background
 
 
 
 <ul>
 <li>
 Physical-world data: 5+ years in data science working with large-scale, noisy real-world data - environments where data quality and fail modes are constant challenges.
 </li>
 <li>
 Background in high-volume, complex real-world data industries: satellite and geospatial, weather forecasting, industrial IoT, manufacturing, is a bonus.
 </li>
 <li>
 Deep learning familiarity: A strong practical understanding of the implications of working with data derived from deep learning models; specifically the nuances of integrating computer vision outputs into broader statistical simulations. You know where the model can mislead you and how to account for it.
 </li>
 <li>
 Python and SQL: You build analysis pipelines and get to robust outputs independently, without needing a data engineering team to do it for you. This is not an expectation of production ready output.
 </li>
 <li>
 Owned external deliverables: Reports or data products that clients or senior stakeholders have relied on. You understand what makes insight land versus what gets ignored.
 </li>
 <li>
 Built from scratch: You have built methodology and process where none existed, not just inherited and executed. You are comfortable setting standards and navigating ambiguity at pace.
 </li>
 <li>
 People leadership: You have managed or mentored at least one person and have a clear view of what good looks like. You can set a standard and give others the structure to work within it.
 
 
 
 </li>
 </ul>
 
 What Success Looks Like
 
 
 
 
 90 days:
 
 You have owned at least one contracted Deepnest deliverable end-to-end. Your team has clear scope and is working effectively. You have a clear picture of the current methodology, where the metric engine stands, and where the gaps are.
 
 
 
 6 months:
 
 The next iteration of the proprietary metric engine and data modelling framework is implemented. A repeatable delivery process is in place and documented.
 
 
 ________________________________________________________________
 
 
 
 About Greyparrot
 
 
 
 The world is in a waste crisis. Currently we produce 2.1 billion tons of solid waste per year. Data collection of the waste we produce is non-existent, meaning no systematic transparency and no accountability. It means that recycling targets are not upheld, dumping of waste into our oceans remains nobody's responsibility, recyclables get sent to landfill or incineration, and producers get away with sub-standard packaging. Thus, recycling rates stubbornly remain at 10% and, unless we change, by 2040 the plastic stock in the ocean will have quadrupled - a problem that already costs society $1.5 trillion each year.
 
 
 Our mission is to digital waste flows to accelerate the circular economy. Currently, our camera system and AI software are deployed in recycling plants and waste facilities around the world to measure material flows and provide waste analytics. We have compiled a team of experts to deploy our technology and we&rsquo;re looking to expand our team.
</div>

Accenture

<div>YOU ARE
As an experienced and senior AI/LLM Architect, you will play a pivotal in designing and delivering end-to-end AI platform architectures that power the modern, reinvented enterprise. Operating at the intersection of business and engineering, you will own the technical design of advanced AI systems &mdash; spanning classical machine learning, generative AI, and agentic systems &mdash; ensuring they are purposefully architected to meet client business objectives and enterprise-grade standards.
Within this scope, you will take deep ownership of one or more critical architecture domains &mdash; such as agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, or model platforms and inference &mdash; serving as the lead authority in your domain across client engagements. You will develop and maintain specialized expertise in the technologies, patterns, and emerging practices within your domain, bringing that depth to bear in shaping architecture decisions, accelerating delivery, and building reusable assets that extend across the practice. You will evaluate, select, and apply the right design patterns, technical frameworks, and tools within your domain and across the broader AI architecture &mdash; ensuring cohesion across the full system. This includes architecting AI agents encompassing multi-agent orchestration, tool use, skills use, and memory systems, as well as the integration of fine-tuned foundation models and classical ML models into scalable, production-ready platforms.
A critical dimension of this is owning the design of a comprehensive AI context layer &mdash; drawing on enterprise knowledge bases, structured and unstructured data sources, and domain-specific content &mdash; to ground AI systems in the realities of each client's business and ensure outputs are accurate, trustworthy, and impactful. As the technical authority on your domains, you will lead architecture decisions and be accountable for ensuring systems meet rigorous non-functional requirements across security, observability, governance, performance, and scalability. You will produce and own the architecture artifacts that shape delivery &mdash; including architecture decision records (ADRs), component and data flow diagrams, and integration specifications &mdash; and provide the technical leadership that enables cross-functional teams of data engineers, ML engineers, and application developers to execute with clarity and confidence.
Your contributions will be instrumental in shaping how clients adopt and scale AI, pushing the boundaries of what these systems can achieve and delivering measurable, lasting business value.
THE WORK<ul><li>
Translate business strategy into a technical vision by defining the non-functional requirements (NFRs) necessary to meet operational goals for performance, reliability, and cost.</li><li>
Lead stakeholder workshops to align on technical feasibility, define project scope, and manage expectations with clients and leadership.</li><li>
Drive the technology selection process, evaluating build-vs-buy decisions for AI platforms (e.g., Arize, LangSmith) and foundational models.</li><li>
Architect model- and tool-agnostic multi-agent systems governed by an MCP Control Plane.</li><li>
Design and implement the Agent Registry as the mandatory system of record and the AI Gateway for runtime policy enforcement.</li><li>
Design and implement a certification gate to ensure no uncertified agents enter production, validating identity, policies, and evaluation metrics.</li><li>
Design, implement, and abstract core agent services, including a first-class abstracted memory service with semantic, episodic, and procedural endpoints.</li><li>
Architect the end-to-end data pipeline for AI systems, including data ingestion, preprocessing, and synchronization for fine-tuning and RAG.</li><li>
Design and implement the context layer&mdash;spanning knowledge graphs, vector search, and semantic retrieval&mdash;to create reliable, grounded RAG pipelines.</li><li>
Architect foundation model adaptation strategies, including dynamic, cost-and-performance-aware model routing and selection.</li><li>
Design, implement, and prototype high-throughput, low-latency inferencing solutions using techniques like response caching and request batching.</li><li>
Define security, governance, and observability as centrally-enforced, by-design controls for all AI systems.</li><li>
Architect a robust, defense-in-depth security framework, including per-agent identity with IAM/IAP binding and layered guardrails.</li><li>
Design and implement FinOps controls enforced at the AI Gateway, including token budgets, cost-center labeling, and threshold alerts.</li><li>
Establish the framework for comprehensive system evaluation, adopting productized tools and instrumenting observability with OTel</li><li>
Define and maintain the enterprise-wide AI reference architecture, reusable design patterns, and a library of approved software components.</li><li>
Independently design, implement, build, and deliver proof-of-concept prototypes and foundational software components to validate architectural decisions.</li><li>
Produce and own authoritative architecture artifacts, including blueprints, sequence diagrams, design specifications, and Architectural Decision Records (ADRs).</li><li>
Mentor and guide cross-functional engineering teams (data, ML, application) on architectural best practices and design patterns.</li><li>
Continuously research and integrate emerging AI patterns, frameworks, and technologies to maintain a forward-looking architecture.</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree or equivalent</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Experience in designing &amp; deploying enterprise grade advanced AI solutions using agentic, generative and classical AI/ML using at least one cloud vendor.</li><li>
Experience in the Agentic, LLM and Generative AI space.</li><li>
Coding experience using Python</li><li>
Experience architecting and operationalizing LLM driven application architecture patterns.</li><li>
Expertise in coding engineering, machine learning, deep learning and NLP solutions and applications.</li><li>
Experience as a machine learning architect in the industry designing big data, machine learning. large scale analytical engineering solutions.</li></ul></div>

AI LLM Technology Architecture Manager

<div>YOU ARE
As a hands-on Infrastructure Architect, you are an experienced engineer with several years in infrastructure engineering who now takes on more complex, higher-impact work designing and optimizing the AI and machine learning infrastructure that powers real-world applications. Working alongside senior architects and engineers &mdash; and increasingly leading your own workstreams &mdash; you apply proven skills in coding, testing, configuring, deploying, monitoring, and troubleshooting AI systems and the infrastructure they run on. Day to day, you architect and optimize infrastructure components, write and review code and deployment scripts, design and tune cloud and on-premises compute resources such as GPU clusters and distributed training environments, deploy AI systems and models into production, and build and optimize data pipelines that feed AI and ML workflows. You optimize the computational stack for performance, cost, power, and scalability, monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and independently troubleshoot and resolve complex issues across the stack. You also mentor junior engineers, contribute to architectural decisions, and help establish best practices. This is a hands-on, ownership-driven where you apply and deepen your expertise across modern tools and platforms &mdash; including container orchestration, model serving, CI/CD pipelines, InfraOps, MLOps, and AI monitoring &mdash; while making meaningful contributions to infrastructure that enables AI-driven business outcomes.
THE WORK<ul><li>
Write, review, and debug code, scripts, and infrastructure-as-code for AI infrastructure, automation, and tooling, setting standards for quality across the team.</li><li>
Architect, configure, and provision compute resources across cloud and on-premises environments, including GPU clusters and distributed training setups, optimizing for performance and utilization.</li><li>
Design and maintain deployment automation and CI/CD pipelines to support reliable, repeatable releases of AI systems, models, and applications.</li><li>
Deploy AI systems, models, and data pipelines into production, defining and improving the processes and best practices others follow.</li><li>
Lead container orchestration and model serving using tools such as Docker, Kubernetes, and model deployment frameworks.</li><li>
Architect and optimize the computational stack for performance, power, cost, and scalability, balancing trade-offs against business goals.</li><li>
Evaluate and select tools, frameworks, and platforms, making recommendations that shape the infrastructure roadmap.</li><li>
Integrate AI models and systems into existing enterprise systems, ensuring interoperability, security, and regulatory compliance.</li><li>
Own AI monitoring and infrastructure health across InfraOps and MLOps, tracking performance, reliability, and utilization, and driving remediation.</li><li>
Independently troubleshoot and resolve complex issues across the computational stack &mdash; hardware, networking, software, and models &mdash; and lead root-cause analysis.</li><li>
Mentor junior engineers and lead code reviews, providing technical direction and supporting their growth.</li><li>
Define and document architecture standards, processes, and procedures, and apply security, cost-efficiency, and scalability best practices across the infrastructure.</li></ul></div>
 
<div>EDUCATION<ul><li>
Bachelor's Degree in Computer Science, Computer Engineering, related Engineering field</li></ul>
BASIC (REQUIRED) QUALIFICATION<ul><li>
Experienced with coding, building, monitoring, troubleshooting applications of AI/ML models; selecting, designing and infrastructure for deploying and running them on premise or on public cloud.</li><li>
Strong understanding of AI and machine learning as a subject.</li><li>
Strong understanding of computing infrastructure a subject, preferred knowledge of AI infrastructure.</li><li>
Proficient in programming languages such as Python, Java, or C++.</li><li>
Experience with data pipeline and workflow management tools (e.g., Apache Airflow, Kubeflow).</li><li>
Strong problem-solving skills and ability to work in a fast-paced environment.</li><li>
Excellent communication and collaboration skills.</li><li>
Experience in AI/ML infrastructure engineering or related roles on a hyperscaler platform for deploying large scale solutions.</li></ul></div>

AI Infrastructure Architecture Specialist

Liberis

<div>Some key info for you about Liberis:

 We were founded in 2007

 We have provided over $3bn of funding to small businesses so far

 We have been named in CNBC &amp; Statista Top 150 UK Fintechs for 2025

 We're a global team, with a dynamic presence in 6 key locations around the world

 We're a thriving community of over 290 innovative minds

 We're a vibrant melting pot, celebrating over 27 nationalities in our team

 Our team brings experience from over 740 previous companies, from startups to global giants

 We have just been named as one of FinTech's Finest 50 by Welcome to the Jungle

 We're proud to be an accredited Real Living Wage employer, ensuring everyone is paid fairly for the great work they do! 


Our Product &amp; Engineering Team:

Liberis is building the embedded finance platform that lets partners around the world offer innovative funding products to their small business customers. We're a growth-stage fintech with teams in London, Nottingham, Atlanta, Stockholm, Munich and Mumbai, and we're building a global Product, Data &amp; Engineering team that thrives on autonomy, ownership, and is focused on impact! Our teams solve real-world problems for small businesses, shaping products that unlock opportunity at scale.

Engineering is going through an AI-first transformation, rethinking how teams are structured and how they ship. It's changing what a small team can do! We empower our teams to make decisions, move fast, and take full responsibility for the solutions they deliver. You'll join a team where curiosity is encouraged and collaboration across Product, Data, Delivery and Engineering is the norm. 


About our Data &amp; Insights Team:

We exist to build the data platforms and analytics that enable every decision at Liberis to be data-informed&mdash;and increasingly, to power AI and ML capabilities across the company!

We're building composable, reliable data platforms that scale&mdash;from ingesting partner transaction data and event streams, to powering analytics dashboards, to feeding ML models with real-time features. We're also supporting the AI/ML platform team with reliable, low-latency feature pipelines and model serving infrastructure.

We're collaborative, pragmatic, and we value moving fast by fixing the right problems&mdash;not over-engineering, but building to last! 


The team is made up of three functions:

Data Platform Engineering: Building and scaling ELT pipelines, managing data infrastructure on GCP, and creating the foundation for analytics and ML feature stores. You'll be part of a small, high-performing team of platform engineers focused on reliability, scale, and developer velocity.

Analytics Engineering: Transform raw data into trusted models using DBT and SQL, powering self-serve analytics and business intelligence for stakeholders across the company.

Data &amp; Business Intelligence: Build dashboards, partner-facing reports, and insights that drive business decisions and revenue outcomes. 


What you'll get to do in the role:
<ul>
<li>Design, build, and maintain resilient data pipelines that ingest data from Azure SQL, SaaS platforms, and event streams into BigQuery.</li>
<li>Write Python code using DLT to define declarative, testable, version-controlled pipelines - no low-code tools, real engineering.</li>
<li>Build and operate ML feature pipelines - low-latency, real-time data streams that feed ML models with accurate, fresh features.</li>
<li>Own the operational health of systems you build - monitoring, alerting, error handling, and incident response. When the data pipeline goes down, merchant credit decisions and ML model predictions suffer.</li>
<li>Collaborate with analytics engineers to understand data needs, validate schema design, and establish data quality standards that both analytics and ML rely on.</li>
<li>Partner with the AI/ML platform team to design feature stores, streaming feature infrastructure, and model serving pipelines that power Liberis' decisioning engine.</li>
<li>Identify and execute optimisation work - improving performance, reliability, and developer velocity without rearchitecting stable systems.</li>
<li>Mentor junior engineers, helping them grow as engineers and supporting their career development.</li>
<li>Participate in technical decisions about platform direction - infrastructure choices, tooling, architecture trade-offs.</li>
<li>Work cross-functionally with product teams, analytics engineers, BI specialists, and the ML platform team to shape data requirements and platform capabilities.</li></ul> 


What we think you'll need:
<ul>
<li>Proven experience within data engineering roles -building and operating data pipelines at scale</li>
<li>Hands-on experience building Modern Data Stack architectures - you understand the layers: ingestion, warehouse, transformation, orchestration, reverse ETL. You've worked with tools like DLT/Fivetran/Airbyte (ingestion), BigQuery/Snowflake/Redshift (warehouse), DBT (transformation), Airflow/similar (orchestration).</li>
<li>Strong Python programming - you write clean, testable, maintainable code with solid error handling and logging.</li>
<li>Fluent SQL - you can write complex queries, understand execution plans, and optimize for performance and cost.</li>
<li>Experience with cloud data platforms - you've built data warehouses in BigQuery, Redshift, Snowflake, or similar; you understand distributed processing, partitioning, cost optimization, and data governance.</li>
<li>Experience with infrastructure-as-code tools (Terraform, CloudFormation, Pulumi) or equivalent - you version control infrastructure and deploy it via CI/CD pipelines.</li>
<li>Experience working in fast-moving environments where requirements evolve and you adapt quickly without losing sight of reliability.</li>
<li>Understanding of DevOps principles - you think in terms of observability, resilience, incident response, and operational excellence. You can set up monitoring and alerting that actually matters.</li></ul> 


Bonus points if you have:
<ul>
<li>Experience with DLT or similar declarative ELT frameworks; experience with Google Cloud Platform ecosystem (BigQuery, Cloud Run, Pub/Sub, Dataflow); experience with Kafka, Pub/Sub, or event streaming platforms; experience scaling data systems from 0 to 100M+ events/day; experience implementing data quality frameworks (Great Expectations, dbt tests, custom monitoring); background in fintech or high-stakes data reliability environments where data quality directly impacts revenue.</li>
<li>Experience working with distributed, asynchronous teams across timezones; experience in India tech ecosystem or building in resource-constrained environments; experience migrating from legacy data infrastructure (Azure ADF, traditional ETL) to modern cloud-native stacks.</li></ul> 

<div>
Career development is really important to us here at Liberis, with progression opportunities for both individual contributors and people managers. You can have a look through our Engineering Career Framework via this link</div>
<div> 
Our hybrid approach 
Working together in person helps us move faster, collaborate better, and build a great Liberis culture. Our hybrid working policy requires team members to be in the office at least 3 days a week. At Liberis, we embrace flexibility as a core part of our culture, while also valuing the importance of the time our teams spend together in the office. 
 
#LI-FC1</div></div>

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