mathematics instructor

flydubai

<div>MAIN OBJECTIVE OF ROLE: To ensure regulatory compliance, governance, oversight, and coordination of the Multi-Crew Pilot License (MPL) program in accordance with GCAA regulations, approved training manuals, operator requirements, and applicable international aviation standards, while supporting the development, implementation, monitoring, and continuous improvement of MPL training compliance and regulatory activities. KEY RESPONSIBILITIES:
<ul><li>Ensures MPL program compliance with GCAA CAR-FCL, CAR-OPS, and IOSA requirements and approved training frameworks.</li><li>
Monitors regulatory updates and assess operational impact on MPL training, manuals, procedures, and approvals.</li><li>
Maintains regulatory compliance tracking for MPL program activities, records, approvals, deviations, and corrective actions.</li><li>
Coordinates preparation and submission of regulatory documentation, approvals, exemptions, and revisions related to MPL operations and training.</li><li>
Supports regulatory audits, inspections, and authority reviews related to MPL activities by coordinating evidence, responses, and required follow-up actions.</li><li>
Maintains MPL training manuals, policies, procedures, syllabi, and associated controlled documentation.</li><li>
Ensures training records, instructor qualifications, assessments, and competency documentation remain compliant and audit ready.</li><li>
Monitors validity and alignment of simulator approvals, training devices, courseware, and training standards.</li><li>
Supports implementation and governance of competency-based training and assessment methodologies.</li><li>
Acts as focal point between Flight Operations Training, Safety, Compliance Monitoring, Quality Assurance, and regulatory authorities on MPL-related matters.</li><li>
Coordinates with internal departments and external training providers to ensure compliance alignment and timely resolution of findings.</li><li>
Supports management reviews, governance meetings, and compliance reporting related to MPL program performance.</li><li>
Tracks audit findings, observations, corrective action plans, and closure status for MPL-related compliance activities.</li><li>
Supports safety investigations, occurrence reviews, and risk assessments impacting MPL training operations.</li><li>
Prepares regulatory reports, compliance dashboards, KPI tracking, and management summaries to support visibility of approvals, instructor currency, trainee progression, authority correspondence, trends, and potential compliance risks.</li></ul>
QUALIFICATIONS:
<ul><li>Bachelor's Degree (3+ years)</li><li>
Fluent in English</li><li>
Minimum 1 year experience in corporate.</li><li>
Years with qualifications: 3 - 5 years</li></ul>
COMPETENCIES:
<ul><li>Customer Focus</li><li>
Teamwork</li><li>
Effective Communication</li><li>
Personal Accountability &amp; Commitment to achieve</li><li>
Resilience &amp; Flexibility (Can do attitude)</li></ul>
ISR Requirements: Reads and complies with the ISR policies of the Company and diligently reports any weakness or incidents to the respective Line Manager or the Information Security team. Completes all required ISR awareness sessions and follows associated guidelines in the day-to-day business operations.</div>

Officer - MPL Policies & Regulations

The Emirates Group

<div><h5 class="jobSectionHeader">Job Purpose 
</h5> At Emirates Group our Analytics Centre of Excellence (ACoE) is a centralized unit that provides data and analytics support to Emirates Group businesses. This allows our businesses to make better decisions by using data and analytics to understand our customers, operations, and markets.The ACoE is an essential part of Emirates Group's digital transformation strategy. The unit is helping Emirates Group to become a more data-driven organization and to make better decisions by using data and analytics. 
 
As a Data Scientist Team Lead you will manage and drive data science projects through the application of mathematical modelling, machine learning, problem structuring and big data analysis to solve complex business problems across the Emirates Group portfolio. The role will require strong skills in computer science, mathematics, statistics and creative problem solving. The job holder is a focused individual who is able to transcend the world of theory, bring it to life in the business context and collaborate successfully with cross functional teams including academic researchers, business stakeholders and information technology teams. 
 
In this role you will:
<div><ul><li>Manage team of Data Scientists, ensuring the culture, strategy and vision are passed to the teams. Aligned with the DS and EA leadership, review team capabilities and establish training/mentoring plan.
</li><li>Support and drive DS execution within team, targeting and developing relationships with the key internal stakeholders and clients to continue to drive practice demand growth.
</li><li>Derive business insight from large, structured, semi-structured and unstructured, data sets and work with business stakeholders to deploy insights derived from this analysis to extract commercial value.
</li><li>Develop, formulate and execute big data models to solve complex business problems. Research, design, implement and validate cutting-edge algorithms to achieve targeted business outcomes using mathematical models, machine learning, operational research theories and dynamic simulations.
</li><li>Build prescriptive models to identify multi-dimensional trends and patterns to understand our customers, optimise revenue and demand management plus drive efficiencies across the operational environment including aircraft operations, engineering, workforce management and catering.
</li><li>Work with cross-functional team members, combining computer science and mathematics, to identify and inject high-value actionable insights into business processes at their point of highest impact. Work in quick iterations, using the techniques and algorithms best suited for solving the challenging problems of the aviation business.
</li><li>Develop and recommend enhancements to existing tools with the goal of advancing the data science capabilities of the organisation. Perform validation and testing of models to ensure adequacy and reformulate models as necessary.
</li><li>Clearly document and communicate the outcomes of the data science initiatives and make sure that the insights and findings can be clearly understood by business stakeholders and translated into actions.
</li>Inspire and promote the adoption of advanced analytics and data science across the entirety of our organization and provide expertise on mathematical concepts for the broader applied analytics team. 
<li>
</li></ul></div> 
<h5 class="jobSectionHeader">Qualification
</h5>To be considered for the role, you must meet the below requirements:
 Qualifications:
<div><ul><li>MSc/PhD in a relevant field such as data science, operations research, mathematics, science, data mining, artificial intelligence, applied statistics, machine learning or economics.
 </li></ul></div>Experience:
<div><ul><li>7-10+ years of hands-on experience in the Analytics ecosystem including:
</li></ul></div><div><div><div><div><ul><li>Experience Managing Data Science or Analytics Teams or acting as Manager in a consulting company.
</li></ul><ul><li>3-5 years hands-on experience with big data technologies (Spark and Hadoop ecosystem) and deep learning frameworks (Tensorflow, Keras, etc.).
</li></ul><ul><li>5-7 years hands-on experience with SQL, R, Python.
</li></ul><ul><li>2-4 years hands-on experience with DevOps frameworks (Git, CI/CD, Docker).
</li></ul><ul><li>3-5 years hands-on experience building libraries and packages to be re-used by others.
</li></ul><ul><li>3-5 years hands-on experience building DS Solutions for production usage.
</li></ul><ul><li>Team player with a problem-solving attitude.
</li></ul><ul><li>Experience of leading &amp; delivering advanced analytical projects to large, complex organisations in a multi-functional environment.
</li></ul><ul><li>Able to challenge and review the quality and value generated from analytical output.
</li></ul>Focus on driving business value for business clients to support long term relationships. Ability to grow teams, ensuring the culture and strategy gets followed by the new hires. 
<ul><li>
</li></ul></div></div></div></div><div></div> 
Join us in a management role and enjoy an attractive tax-free salary. On top of our generous travel benefits, including discounted flights and hotel stays around the world, this managerial role also has an excellent leave and healthcare package. That&rsquo;s on top of transport benefits, life insurance and more.
<h5 class="jobSectionHeader">Salary &amp; benefits
</h5>Find out what it&rsquo;s like to live and work in our fast-paced, cosmopolitan home city in the Dubai Lifestyle section of our website www.emirates.com/careers</div>

Data Scientist Team Lead –

Dyson

<div>
 
 About Us
 
 
 
 At Dyson, we&rsquo;re driven by a relentless pursuit of innovation&mdash;pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson&rsquo;s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.
 
 
 You&rsquo;ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.
 
 
 
 About the role
 
 
 We are looking for a specialized Data Intelligence Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
 </li>
 <li>
 Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.
 </li>
 <li>
 Quality Assurance &amp; Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.
 </li>
 <li>
 Tooling &amp; Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.
 </li>
 <li>
 Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.
 </li>
 <li>
 Set up and maintain robust data preparation infrastructure&mdash;optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.
 </li>
 <li>
 Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You&rsquo;ll help transform raw data into actionable insight, supporting both research and deployment.
 </li>
 <li>
 Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.
 
 
 
 </li>
 </ul>
 
 About you
 
 
 
 <ul>
 <li>
 At least 3+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.
 </li>
 <li>
 Proficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).
 </li>
 <li>
 Automated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).
 </li>
 <li>
 Data Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).
 </li>
 <li>
 Cloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.
 </li>
 <li>
 Version Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.
 </li>
 <li>
 Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.
 </li>
 <li>
 Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).
 </li>
 <li>
 Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).
 </li>
 <li>
 Strong background in feature engineering, data analysis, and data visualization&mdash;comfortable using tools like Jupyter, Tableau, or Power BI.
 </li>
 <li>
 Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.
 </li>
 <li>
 Able to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.
 </li>
 <li>
 Bachelor&rsquo;s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.
 
 
 
 </li>
 </ul>
 Dyson is an equal opportunity employer. We know that great minds don&rsquo;t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.
</div>

Data Intelligence Machine Learning Engineer

TALENTMATE

<div>
 
 Job Description
 
 
 
 
 About Us
 
 
 
 At Dyson, we&rsquo;re driven by a relentless pursuit of innovation&mdash;pushing boundaries in engineering, AI, and robotics. Our new Data Intelligence team sits at the heart of this mission: shaping Dyson&rsquo;s future through data. Here, we blend creativity, precision, and audacity to power intelligent products. We craft data strategies and pipelines that fuel the next generation of connected devices.
 
 
 You&rsquo;ll work alongside brilliant minds from Dyson global engineering team and external software/hardware partners in an environment built for exploration, discovery, delivery and impact.
 
 
 
 About the role
 
 
 We are looking for a specialized Data Intelligence Machine Learning Engineer to design and implement in-house tools that automate our data labelling pipelines. Your primary goal will be to reduce our reliance on manual annotation by leveraging techniques like Active Learning, Weak Supervision, and Synthetic Data Generation. You will bridge the gap between raw data collection and model-ready datasets, ensuring high-quality labels at scale.
 
 
 Key Responsibilities
 
 
 
 <ul>
 <li>
 Architect Labelling Pipelines: Design and deploy end-to-end automated labelling systems using frameworks like Snorkel, Cleanlab, or custom active learning loops.
 </li>
 <li>
 Develop "Human-in-the-Loop" (HITL) Systems: Build interfaces and workflows where models pre-label data and humans only intervene on high-uncertainty samples.
 </li>
 <li>
 Quality Assurance &amp; Denoising: Implement algorithmic checks to identify and correct mislabelled or "noisy" data within existing datasets.
 </li>
 <li>
 Tooling &amp; Integration: Collaborate with software engineers to integrate labelling tools with our existing data lakes and ML training infrastructure.
 </li>
 <li>
 Model Optimization: Fine-tune "teacher" models to generate high-quality pseudo-labels for "student" models.
 </li>
 <li>
 Set up and maintain robust data preparation infrastructure&mdash;optimising for data quality, speed, and seamless integration with downstream MLOps pipelines.
 </li>
 <li>
 Perform data visualization and in-depth analysis using advanced data and feature engineering techniques. You&rsquo;ll help transform raw data into actionable insight, supporting both research and deployment.
 </li>
 <li>
 Work closely with Data Scientists, Software Engineers, and Product teams to ensure high data quality and usability across products and projects.
 
 
 </li>
 </ul>
 
 About you
 
 
 
 <ul>
 <li>
 At least 3+ years of professional experience in Machine Learning engineering, specifically focused on data centric-AI or computer vision/NLP pipelines.
 </li>
 <li>
 Proficiency in Python: Mastery of the Machine Learning stack (PyTorch or TensorFlow, NumPy, Pandas, Scikit-learn).
 </li>
 <li>
 Automated Labelling Expertise: Proven experience with Weak Supervision (labelling functions) or Active Learning strategies (uncertainty sampling, diversity sampling).
 </li>
 <li>
 Data Engineering: Experience with SQL and NoSQL databases, and managing large-scale unstructured data (images, text, or audio).
 </li>
 <li>
 Cloud Infrastructure: Familiarity with AWS (SageMaker Ground Truth), GCP (Vertex AI), or Azure ML labelling services.
 </li>
 <li>
 Version Control for Data: Experience with DVC (Data Version Control) or similar tools to track dataset iterations.
 </li>
 <li>
 Hands-on expertise building auto-labelling solutions or working with large-scale data annotation workflows.
 </li>
 <li>
 Advanced skills in Python (and/or other relevant languages), and experience with key ML/data science libraries (e.g. TensorFlow, PyTorch, scikit-learn, pandas).
 </li>
 <li>
 Experience designing, deploying, and maintaining scalable data pipelines, including data cleansing, transformation, and storage (cloud, on-prem, or hybrid).
 </li>
 <li>
 Strong background in feature engineering, data analysis, and data visualization&mdash;comfortable using tools like Jupyter, Tableau, or Power BI.
 </li>
 <li>
 Great communicator who documents solutions clearly and collaborates effortlessly across technical and non-technical teams.
 </li>
 <li>
 Able to balance speed and quality, stay curious about new developments, and deliver results in a fast-moving environment.
 </li>
 <li>
 Bachelor&rsquo;s or Master's degree in computer science, Engineering, Mathematics, Data Science, or a related field.
 
 
 </li>
 </ul>
 Dyson is an equal opportunity employer. We know that great minds don&rsquo;t think alike, and it takes all kinds of minds to make our technology so unique. We welcome applications from all backgrounds and employment decisions are made without regard to race, colour, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other any other dimension of diversity.
 
 
 Job Details
 
 
 Role Level: Not Applicable Work Type: Full-Time Country: United Arab Emirates City: Dubai Company Website: http://careers.dyson.com Job Function: Engineering Company Industry/
 
 
 Sector: Appliances Electrical and Electronics Manufacturing
 
 
 What We Offer
 
 
 
 About The Company
 
 
 
 Searching, interviewing and hiring are all part of the professional life. The TALENTMATE Portal idea is to fill and help professionals doing one of them by bringing together the requisites under One Roof. Whether you're hunting for your Next Job Opportunity or Looking for Potential Employers, we're here to lend you a Helping Hand.
 
 
 Report
 
 
 Disclaimer: talentmate.com is only a platform to bring jobseekers &amp; employers together. Applicants are advised to research the bonafides of the prospective employer independently. We do NOT endorse any requests for money payments and strictly advice against sharing personal or bank related information. We also recommend you visit Security Advice for more information. If you suspect any fraud or malpractice, email us at abuse@talentmate.com.
</div>

<div><h5 class="jobSectionHeader">Job Purpose
</h5>At Emirates, we believe in connecting the world, to and through our global hub in Dubai, and in constantly innovating to ensure our customers 'Fly Better'.
Emirates Group IT thrives on the dynamic nature of technology. Being pioneers in aviation innovation, we're always at the forefront, pushing boundaries. We're on the lookout for exceptional IT professionals to fortify our position as leaders in the industry. Embark on a journey with the world's largest international airline and become a vital part of our cutting-edge information and technology team as a Senior Quality Assurance Engineer.
As a Senior Quality Assurance Engineer, you will join our Quality Engineering team, where quality lies at the heart of how we operate. We value diverse perspectives, backgrounds and ways of thinking, and we are building a team of quality engineers who assure the operationally critical systems that keep Emirates running &mdash; from real-time operational decision-making to the security platforms that protect our people, aircraft and infrastructure.
We are looking for professionals who judge quality by operational reality, not by test counts. You will think like the end user and the operation, question assumptions early, and challenge whether a system can be trusted on the day it matters &mdash; under real load, degraded dependencies, and disruption. Strong hands-on experience building automated API and UI tests is essential, alongside a genuine grasp of non-functional quality: performance, resilience, failover and observability.
Our engineering teams use a modern suite of testing tools and methodologies, with CI/CD pipelines and DevSecOps practices embedded throughout. You will work across in-house and vendor-delivered solutions, holding both to Emirates' quality standards rather than accepting completion claims as evidence of readiness.
If you are passionate about shaping the future of quality engineering and helping deliver resilient, predictable operations with no surprises on the day, we invite you to apply and help build world-class operational systems. 
 
 
In this role, you will:
<ul><li>Work closely with architects, software engineers and product owners to translate complex functional and non-functional requirements, and provide early feedback on risks in design and delivery.
</li><li>Own a concise test strategy for your initiative, covering functional, automation, non-functional and environment/data needs, aligned to the wider Test Like Ops approach.
</li><li>Prioritise testing by business impact and operational risk, focusing effort on the user journeys and failure paths that hurt the operation most.
</li><li>Build, maintain and adapt automation frameworks and harnesses, integrated into CI/CD for fast, reliable feedback.
</li><li>Push for production-like environments and realistic test data &mdash; including service virtualisation or stubbing where dependencies are unstable or vendor-controlled.
</li><li>Hold vendor-delivered solutions to defined entry/exit and acceptance criteria, demanding operationally meaningful evidence of readiness.
</li><li>Feed production incidents back into test design and regression so the same failures don't recur.
</li>Liaise with other release train teams to manage testing of cross-dependencies, and ensure test tools and metrics feed program and enterprise dashboards accurately. 
<li>
</li></ul> 
<h5 class="jobSectionHeader">Qualification 
</h5> 
To be considered for the role, you must meet the requirements below:
<ul><li>Information Technology Degree or Honours or equivalent Degree in a relevant field such as Computer Science, Computational Mathematics, Computer Engineering, or Software Engineering.
</li><li>5+ years in quality engineering, including recent experience assuring operationally critical or 24/7 systems where downtime carries real business impact.
</li><li>Experience in Software Quality Engineering or equivalent, looking after multiple products or an end-to-end initiative.
</li><li>Extensive hands-on experience developing UI and API automation frameworks, proficient in at least two languages (e.g. Java, Python, JavaScript) with strong coding practices.
</li><li>Practical experience with automation tooling (e.g. Selenium/WebDriver, Cucumber/Gherkin, JUnit/TestNG) and CI/CD tools (Jenkins, GitLab, Azure DevOps or equivalent).
</li><li>Demonstrable non-functional testing depth &mdash; performance, resilience, failover/recovery and observability &mdash; treated as part of release confidence.
</li><li>Strong expertise in risk-based, context-driven and exploratory testing, using recognised heuristics.
</li><li>Experience delivering within a SAFe / Agile Release Train or equivalent PI-cadenced model.
</li><li>Experience assuring vendor-delivered solutions against measurable acceptance criteria.
</li>Desirable: exposure to security or compliance-driven environments (access control, surveillance, identity/integration platforms); test data management (synthetic generation/masking); production-like environment setup; and practical use of AI in QE. 
<li>
</li></ul> 
Leadership Role: No
<h5 class="jobSectionHeader">Salary &amp; benefits
</h5>Join us in Dubai and enjoy an attractive tax-free salary and travel benefits that are exclusive to our industry, including discounts on flights and hotel stays around the world. Find out what it's like to live and work in our fast-paced, cosmopolitan home city in the Dubai Lifestyle section of our website www.emirates.com/careers</div>

Senior Quality Assurance Engineer

talabat

<div>
 Since launching in Kuwait in 2004, talabat has become the region&rsquo;s leading on-demand delivery app, serving millions of customers across eight countries. Our Quick Commerce (QC) Hub powers the grocery and convenience delivery experience &mdash; getting everyday essentials to customers&rsquo; doors in minutes, not hours.
 
 
 Behind every delivery is data. Our QC data team works at a scale most data scientists only read about: millions of daily transactions, thousands of partners, and decisions that directly shape how people across the Middle East get their groceries delivered. We&rsquo;re building an analytics-first culture where every product and business decision is grounded in evidence.
 
 
 
 Job Description
 
 
 
 
 Why This Role
 
 
 
 As a Data Scientist on the QC Hub team, you&rsquo;ll be the analytical brain behind one of talabat&rsquo;s fastest-growing verticals. You won&rsquo;t just crunch numbers &mdash; you&rsquo;ll partner directly with product and business leaders to shape strategy, design experiments that affect millions of users, and build the data foundations that power smarter decisions.
 
 
 This is a role for someone who loves turning messy, ambiguous business questions into clean, actionable analysis &mdash; and who gets energy from seeing their insights change how a team operates.
 
 
 
 What Success Looks Like
 
 
 
 <ul>
 <li>
 First 90 days: You&rsquo;ve ramped up on your domain, built relationships with your product and business partners, understood the data landscape, and delivered your first actionable analysis.
 </li>
 <li>
 By 6 months: You&rsquo;re the go-to analytical partner for your domain. You&rsquo;re independently designing and running experiments, and stakeholders regularly act on your recommendations.
 </li>
 <li>
 By 12 months: Your work has measurably improved decision quality in your domain. You&rsquo;ve built or refined data models that the team relies on daily, and you&rsquo;re mentoring newer team members on analytical best practices.
 
 
 </li>
 </ul>
 
 What You&rsquo;ll Actually Do
 
 
 
 
 You&rsquo;ll spend roughly:
 
 
 
 <ul>
 <li>
 40% on deep analysis and experimentation &mdash; designing A/B tests, running multivariate experiments, doing deep dives into performance drivers, and turning findings into clear recommendations.
 </li>
 <li>
 30% on data modelling and quality &mdash; building and maintaining the data models that let us measure what matters, profiling source data, and ensuring data reliability.
 </li>
 <li>
 30% working with stakeholders &mdash; partnering with product and business managers to frame the right questions, set meaningful KPIs, and present insights that drive action.
 
 
 </li>
 </ul>
 
 Day-to-day, You&rsquo;ll
 
 
 
 <ul>
 <li>
 Turn ambiguous business questions into structured analytical problems
 </li>
 <li>
 Build and maintain dimensional data models in BigQuery
 </li>
 <li>
 Design, execute, and interpret experiments (A/B and multivariate)
 </li>
 <li>
 Create automated dashboards and reports that stakeholders actually use
 </li>
 <li>
 Challenge assumptions with data &mdash; including your own
 </li>
 <li>
 Collaborate with data engineers on logging and data pipeline quality
 
 
 </li>
 </ul>
 
 You&rsquo;ll Thrive Here If You&hellip;
 
 
 
 <ul>
 <li>
 Love being embedded with business teams, not siloed in a data team
 </li>
 <li>
 Get satisfaction from changing how decisions are made, not just producing reports
 </li>
 <li>
 Are comfortable with ambiguity &mdash; many of your best projects will start as vague questions
 </li>
 <li>
 Care deeply about data quality and are willing to dig into source systems to understand what the data actually means
 </li>
 <li>
 Communicate clearly with non-technical stakeholders
 
 
 </li>
 </ul>
 
 This Might Not Be For You If You&hellip;
 
 
 
 <ul>
 <li>
 Want to build ML models full-time (this role is analytics and experimentation focused)
 </li>
 <li>
 Prefer working independently without regular stakeholder interaction
 </li>
 <li>
 Need clearly defined problems handed to you
 </li>
 <li>
 Are more interested in tools and techniques than business impact
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 
 What You Bring
 
 
 
 
 Education
 
 
 
 Degree in a quantitative field (statistics, mathematics, economics, computer science, engineering, or similar) &mdash; or equivalent practical experience. A postgraduate degree is a plus but not required.
 
 
 
 Must-Haves
 
 
 
 <ul>
 <li>
 Strong SQL skills &mdash; you can write complex queries with window functions, CTEs, and optimise for performance on large datasets
 </li>
 <li>
 Reproducible analysis in Python or R &mdash; you write clean, well-structured analytical code, not one-off scripts
 </li>
 <li>
 Experiment design expertise &mdash; you understand when to use A/B vs. multivariate tests, can calculate sample sizes, and know the pitfalls of statistical testing
 </li>
 <li>
 Full analysis lifecycle experience &mdash; from problem framing through data auditing, analysis, interpretation, and presenting recommendations
 </li>
 <li>
 Data modelling knowledge &mdash; you understand dimensional design and can build models that serve both ad-hoc analysis and automated reporting
 </li>
 <li>
 Product analytics intuition &mdash; you&rsquo;re familiar with metrics like conversion, engagement, and retention, and know how to measure product health
 </li>
 <li>
 3+ years in data science, analytics, or a related quantitative field
 
 
 </li>
 </ul>
 
 Nice-to-Haves
 
 
 
 <ul>
 <li>
 Experience with BigQuery and Google Cloud Platform
 </li>
 <li>
 Data engineering skills (Airflow, dbt, or similar pipeline tools)
 </li>
 <li>
 Experience with ML frameworks (Scikit-learn, XGBoost, LightGBM)
 </li>
 <li>
 Familiarity with modern data tools and AI-assisted analysis workflows
 </li>
 <li>
 Experience in an online consumer product or marketplace environment
 
 
 </li>
 </ul>
 
 Additional Information
 
 
 
 
 What We Offer
 
 
 
 <ul>
 <li>
 Impact at scale &mdash; your work directly affects how millions of people get their daily essentials
 </li>
 <li>
 An analytics-first culture &mdash; a data team that&rsquo;s genuinely invested in analytical excellence, not just dashboarding
 </li>
 <li>
 Modern tooling &mdash; BigQuery, GCP ecosystem, and the freedom to experiment with new approaches including AI-assisted workflows
 </li>
 <li>
 Career growth &mdash; past data scientists on the team have grown into senior individual contributor roles and analytics leads
 </li>
 <li>
 Great Place to Work &mdash; talabat is a certified Great Place to Work across multiple countries in the region
 </li>
 </ul>
</div>

Data Scientist II - Analysis, QC

Delivery Hero

<div>Company Description 
Since launching in Kuwait in 2004, talabat has become the region&rsquo;s leading on-demand delivery app, serving millions of customers across eight countries. Our Quick Commerce (QC) Hub powers the grocery and convenience delivery experience &mdash; getting everyday essentials to customers&rsquo; doors in minutes, not hours.
Behind every delivery is data. Our QC data team works at a scale most data scientists only read about: millions of daily transactions, thousands of partners, and decisions that directly shape how people across the Middle East get their groceries delivered. We&rsquo;re building an analytics-first culture where every product and business decision is grounded in evidence. 
Job Description 
Why This Role
As a Data Scientist on the QC Hub team, you&rsquo;ll be the analytical brain behind one of talabat&rsquo;s fastest-growing verticals. You won&rsquo;t just crunch numbers &mdash; you&rsquo;ll partner directly with product and business leaders to shape strategy, design experiments that affect millions of users, and build the data foundations that power smarter decisions.
This is a role for someone who loves turning messy, ambiguous business questions into clean, actionable analysis &mdash; and who gets energy from seeing their insights change how a team operates.
What Success Looks Like<ul><li>
First 90 days: You&rsquo;ve ramped up on your domain, built relationships with your product and business partners, understood the data landscape, and delivered your first actionable analysis.</li><li>
By 6 months: You&rsquo;re the go-to analytical partner for your domain. You&rsquo;re independently designing and running experiments, and stakeholders regularly act on your recommendations.</li><li>
By 12 months: Your work has measurably improved decision quality in your domain. You&rsquo;ve built or refined data models that the team relies on daily, and you&rsquo;re mentoring newer team members on analytical best practices.</li></ul>
What You&rsquo;ll Actually Do
You&rsquo;ll spend roughly:<ul><li>
40% on deep analysis and experimentation &mdash; designing A/B tests, running multivariate experiments, doing deep dives into performance drivers, and turning findings into clear recommendations.</li><li>
30% on data modelling and quality &mdash; building and maintaining the data models that let us measure what matters, profiling source data, and ensuring data reliability.</li><li>
30% working with stakeholders &mdash; partnering with product and business managers to frame the right questions, set meaningful KPIs, and present insights that drive action.</li></ul>
Day-to-day, you&rsquo;ll:<ul><li>
Turn ambiguous business questions into structured analytical problems</li><li>
Build and maintain dimensional data models in BigQuery</li><li>
Design, execute, and interpret experiments (A/B and multivariate)</li><li>
Create automated dashboards and reports that stakeholders actually use</li><li>
Challenge assumptions with data &mdash; including your own</li><li>
Collaborate with data engineers on logging and data pipeline quality</li></ul>
You&rsquo;ll Thrive Here If You&hellip;<ul><li>
Love being embedded with business teams, not siloed in a data team</li><li>
Get satisfaction from changing how decisions are made, not just producing reports</li><li>
Are comfortable with ambiguity &mdash; many of your best projects will start as vague questions</li><li>
Care deeply about data quality and are willing to dig into source systems to understand what the data actually means</li><li>
Communicate clearly with non-technical stakeholders</li></ul>
This Might Not Be For You If You&hellip;<ul><li>
Want to build ML models full-time (this role is analytics and experimentation focused)</li><li>
Prefer working independently without regular stakeholder interaction</li><li>
Need clearly defined problems handed to you</li><li>
Are more interested in tools and techniques than business impact</li></ul> 

Qualifications 
What You Bring 
Education
Degree in a quantitative field (statistics, mathematics, economics, computer science, engineering, or similar) &mdash; or equivalent practical experience. A postgraduate degree is a plus but not required.
Must-Haves:<ul><li>
Strong SQL skills &mdash; you can write complex queries with window functions, CTEs, and optimise for performance on large datasets</li><li>
Reproducible analysis in Python or R &mdash; you write clean, well-structured analytical code, not one-off scripts</li><li>
Experiment design expertise &mdash; you understand when to use A/B vs. multivariate tests, can calculate sample sizes, and know the pitfalls of statistical testing</li><li>
Full analysis lifecycle experience &mdash; from problem framing through data auditing, analysis, interpretation, and presenting recommendations</li><li>
Data modelling knowledge &mdash; you understand dimensional design and can build models that serve both ad-hoc analysis and automated reporting</li><li>
Product analytics intuition &mdash; you&rsquo;re familiar with metrics like conversion, engagement, and retention, and know how to measure product health</li><li>
3+ years in data science, analytics, or a related quantitative field</li></ul>
Nice-to-Haves:<ul><li>
Experience with BigQuery and Google Cloud Platform</li><li>
Data engineering skills (Airflow, dbt, or similar pipeline tools)</li><li>
Experience with ML frameworks (Scikit-learn, XGBoost, LightGBM)</li><li>
Familiarity with modern data tools and AI-assisted analysis workflows</li><li>
Experience in an online consumer product or marketplace environment</li></ul> 

Additional Information 
What We Offer<ul><li>
Impact at scale &mdash; your work directly affects how millions of people get their daily essentials</li><li>
An analytics-first culture &mdash; a data team that&rsquo;s genuinely invested in analytical excellence, not just dashboarding</li><li>
Modern tooling &mdash; BigQuery, GCP ecosystem, and the freedom to experiment with new approaches including AI-assisted workflows</li><li>
Career growth &mdash; past data scientists on the team have grown into senior individual contributor roles and analytics leads</li><li>
Great Place to Work &mdash; talabat is a certified Great Place to Work across multiple countries in the region</li></ul></div>

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