data science lead

Oredata Yazılım Anonim Şirketi

<div><p>OREDATA is a Digital Transformation &amp; IT Consulting firm with 10+ years of proven expertise and hundreds of successfully implemented projects across the EMEA region. When you join the OREDATA team, you'll be working hand-in-hand with experts focused on tackling digital, operational, analytical &amp; data science challenges with the greatest impact. We foster collaboration with proximity, an agile and autonomous approach and best practices and guiding principles.</p><p>
Read more: https://medium.com/@oredata-engineering</p><p>
Apply and be part of our exciting journey!</p><p></p><p><b>
Requirements</b></p><ul><li>
Bachelor&rsquo;s or Master&rsquo;s degree in Computer Science, Data Science, Statistics, Engineering, or a related field
</li><li>8+ years of professional experience in data science, machine learning, or analytics roles
</li><li>Experience working on AI / Machine Learning projects (model development, data-driven solutions, analytics use cases)
</li><li>Experience leading or coordinating data science teams (team coordination, task prioritization, delivery management)
</li><li>Experience in stakeholder management (business&ndash;technical communication, requirement alignment)
</li><li>Ability to translate business problems into data-driven solutions (problem framing, analytical thinking, solution design)
</li><li>Hands-on experience in data analysis and validation (data exploration, hypothesis testing, insight generation)
</li><li>Experience working as a bridge between business units and technical teams (cross-functional collaboration, communication)
</li><li>Experience using Jira for task tracking, backlog management, and team coordination is a plus
</li><li>Experience managing end-to-end problem-solving processes (problem definition, solution approach, execution coordination)
</li><li>Ability to lead and facilitate meetings, workshops, and decision-making sessions (technical and business alignment)
</li><li>Strong communication skills with a data-driven and result-oriented mindset</li></ul><p></p><p><b>
Get to know us</b></p><p>
If you want to know more about us and what we do, then visit our website: www.oredata.com</p><p><b>
Why Oredata?</b></p><ul><li>
Remote working and flexible time off</li><li>
Open communication, flexibility and start-up spirit</li><li>
Learning &amp; Development opportunities for both personal and professional growth</li><li>
Opportunity to get company paid Professional Certificates (Google Cloud Platform, Confluent Kafka, etc)</li><li>
Access to Online Training Platforms (Udemy, Pluralsight, A Cloud Guru, Coursera, etc.)</li><li>
Dynamic work ecosystem where you can take initiative and responsibility</li><li>
Opportunity to work on international projects</li><li>
Private Health Insurance</li><li>
Birthday Leave Policy</li></ul><p></p><p><b>
Kişisel verileriniz işe alım s&uuml;recinin y&uuml;r&uuml;t&uuml;lebilmesi amacıyla veri sorumlusu sıfatıyla şirketimiz Oredata Yazılım A.Ş. tarafından işlenecektir.</b> Kişisel verilerinizin işlenmesi ve haklarınızla ilgili detaylı bilgiye https://oredata.com/personal-data-protection-policy/ bağlantısı &uuml;zerinden ulaşabilirsiniz.</p></div>

Data Science Team Lead

JPMorganChase

<div><b>JOB DESCRIPTION</b><p><br>
Join us to shape how we measure and improve the software delivery experience across Chase. You will lead a team that turns product and engineering data into clear, trusted insights that influence strategy, investment decisions, and roadmaps. This role offers the opportunity to define measurement frameworks in an area where standards are still evolving, and to build a culture of analytical rigor and practical impact. If you enjoy ambiguous problems, influencing senior stakeholders, and developing talent, you will thrive with us.
</p><p>Job summary
</p><p>As a <b>Vice President, Data Science Lead</b> in the <b>Product, Experience and Technology Analytics</b> team, you will define and execute the analytics strategy that measures developer productivity, technology efficiency, and product value across internal platforms and tools. You will lead data scientists and analysts to build measurement frameworks, dashboards, and models that quantify adoption, engagement, and outcomes across the software development lifecycle. You will partner closely with leaders across Product, Technology, and Finance to translate complex findings into clear narratives that drive decisions. You will set priorities, raise the bar on analytical rigor, and help your team deliver high-impact insights at scale.
</p><p></p><p>Our analytics work focuses on initiatives that improve software delivery and developer workflows, including measurement of CI/CD enhancements and adoption and impact of generative AI solutions embedded in engineering processes. You will help establish consistent definitions and reporting for metrics such as cycle time, throughput, and product adoption funnels, enabling leaders to understand what is working and where to invest next.
</p><p>Job responsibilities
</p><ul><li>Lead, manage, and develop a team of data scientists and analysts through goal setting, coaching, feedback, and performance management.
</li><li>Define and own measurement frameworks that quantify adoption, engagement, feature effectiveness, and value delivery across internal developer platforms and tools.</li><li>
Partner with senior product and engineering leaders to align analytics priorities with developer productivity and technology efficiency objectives.
</li><li>Analyze large, complex datasets (for example, pipeline events, activity logs, and usage telemetry) to identify trends and opportunities across the software development lifecycle.
</li><li>Direct the development of models that quantify the impact of workflow automation and generative AI tools on delivery speed and engineering outcomes. J</li><li>
Guide experiment design and evaluation to test hypotheses on adoption, feature changes, and workflow improvements, ensuring results are methodologically sound.</li><li>
Oversee dashboards and recurring reporting that make key metrics easy to understand and actionable for leaders and teams.</li><li>
Drive scalable analytics engineering pipelines and dependable data flows from source systems to reporting and modeling layers.
</li><li>Communicate analytical findings as clear, decision-ready recommendations for senior stakeholders across Product, Technology, and Finance.
</li><li>Set standards for analytical rigor, documentation, and reusable assets, improving consistency across workstreams.
</li><li>Stay current on product analytics and developer productivity measurement practices, bringing new approaches into the team&rsquo;s work.
</li></ul><p>Required qualifications, capabilities, and skills
</p><ul><li>Bachelor&rsquo;s degree in data science, statistics, computer science, or a related field.
</li><li>Six or more years of experience in data science, product analytics, or a related analytics role.
</li><li>Two or more years of people management experience, including coaching, performance management, and team development.
</li><li>Demonstrated ability to define metrics and measurement frameworks for product adoption, engagement, and value.
</li><li>Proven ability to structure ambiguous problems and deliver clear analytical approaches and outputs.
</li><li>Strong proficiency in SQL and Python for analysis, and experience with data visualization tools used for executive reporting.
</li><li>Experience working with modern data warehouse or lakehouse technologies.</li><li>
Strong foundation in machine learning, statistical modeling, and data mining techniques.
</li><li>Excellent communication and presentation skills, including ability to influence technical and non-technical senior stakeholders.
</li><li>Demonstrated ability to manage competing priorities and deliver results in a fast-paced environment.
</li></ul><p>Preferred qualifications, capabilities, and skills
</p><ul><li>Master&rsquo;s degree or PhD in a quantitative field.
</li><li>Experience leading analytics for platform, infrastructure, or internal tooling products.
</li><li>Familiarity with Agile delivery practices and common work management tools used to run sprints and track delivery.
</li><li>Experience with analytics engineering or orchestration frameworks (for example, dbt or Airflow).
</li><li>Working knowledge of software delivery lifecycle concepts and related operational metrics.
</li><li>Familiarity with AI-assisted development tools used to support coding and delivery workflows.
</li><li>Experience building or scaling an analytics team function, including defining standards and repeatable processes.</li></ul><p>
</p><b>ABOUT US</b><p><br>
Chase is a leading financial services firm, helping nearly half of America&rsquo;s households and small businesses achieve their financial goals through a broad range of financial products. Our mission is to create engaged, lifelong relationships and put our customers at the heart of everything we do. We also help small businesses, nonprofits and cities grow, delivering solutions to solve all their financial needs.
</p><p></p><p>We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.
</p><p></p><p>We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants&rsquo; and employees&rsquo; religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.</p><p></p><p>
Equal Opportunity Employer/Disability/Veterans</p><br>
<br>
<br>
<b>ABOUT THE TEAM</b><br>
<div>
Our Consumer &amp; Community Banking division serves our Chase customers through a range of financial services, including personal banking, credit cards, mortgages, auto financing, investment advice, small business loans and payment processing. We&rsquo;re proud to lead the U.S. in credit card sales and deposit growth and have the most-used digital solutions &ndash; all while ranking first in customer satisfaction.</div><div><br>
<br>
</div><div>The CCB Data &amp; Analytics team responsibly leverages data across Chase to build competitive advantages for the businesses while providing value and protection for customers. The team encompasses a variety of disciplines from data governance and strategy to reporting, data science and machine learning. We have a strong partnership with Technology, which provides cutting edge data and analytics infrastructure. The team powers Chase with insights to create the best customer and business outcomes.</div></div>

VP Data Scientist Lead – Product, Experience and Technology (PXT) Analytics Team

MILAHA

<div><p>Communication
<br>
Internal Communication:
<br>
Internal Business Units
<br>
All Internal Department
<br>
Senior Leadership Team
<br>
Purpose:
<br>
 Innovation in AI Products and Services
<br>
 Customer Experience Enhancement:
<br>
 Data Integration and Democratization
<br>
 Predictive and Prescriptive Analytics
<br>
 Change Management and Adoption of AI
<br>
 Cloud and Scalable AI Infrastructure
<br>
 Data Governance and Ethics
</p><p>External Communication:
<br>
External IT Vendors
<br>
Software Integrators
</p><p>Occupational Health &amp; Safety and Environment
<br>
Accountability:
<br>
Are accountable for their acts and omissions.
<br>
Responsibility:
<br>
To follow agreed safe systems of work; to follow training and instructions; and to report accidents, incidents and near misses.
<br>
Authority:
<br>
To stop work if they think the work is unsafe.</p></div>
<br><div><div>Education &amp; Professional Qualification:
<br>
Bachelor&rsquo;s Degree in Computer Science, Data Science
<br>
Artificial Intelligence, Statistics, Mathematics, Engineering, Information Technology
<br>
Master&rsquo;s Degree (preferred):
<br>
Data Science, Artificial Intelligence or Machine Learning, Computer Science, Business Analytics, Statistics or Applied Mathematics, Operations Research
<br>
Professional Experience:
<br>
<ul><li>8 years of hands-on experience in data science, machine learning, or AI roles, including developing and deploying AI models and algorithms.
</li><li>Experience with machine learning techniques, such as:
</li><li>Supervised/Unsupervised Learning
</li><li>Deep Learning (e.g., Neural Networks, CNNs, RNNs)
</li><li>Natural Language Processing (NLP)
</li><li>Computer Vision
</li></ul>Geographic Experience:
<br>
A plus
<br>
Computer Skills:
<br>
data science, machine learning, AI, including developing and deploying AI models and algorithms
<br>
Language Skills:
<br>
Business fluent English
<br>
Arabic Language is an advantage.</div></div>
<br><div><div>Strategic Leadership &amp; Vision
<br>
<ul><li>Define and execute the organization&rsquo;s Data Science &amp; AI strategy in alignment with business goals.
</li><li>Identify and prioritize opportunities where AI and data driven solutions enhance efficiency, customer experience, product offerings, and revenue generation.
</li><li>Lead the design of a unified data and AI architecture, including the development of a scalable Data Lakehouse, semantic layers, feature stores, and streaming/batch data integration.
</li><li>Drive the Generative AI roadmap&mdash;selecting LLM providers, designing RAG architectures, and enabling enterprise wide AI capabilities.
</li><li>Oversee FinOps practices to optimize cloud and compute costs associated with AI, GPU workloads, and large scale data storage.
</li></ul>AI &amp; Data Engineering Execution
<br>
<ul><li>Oversee the end to end development, validation, deployment, and lifecycle management of machine learning and AI models.
</li><li>Implement CI/CD/CT pipelines to ensure automated, continuous, and reliable model updates.
</li><li>Build and maintain vector database infrastructure to support semantic search, contextual AI, and long term knowledge retrieval.
</li><li>Ensure data pipelines, platforms, and architectures are optimized for performance, scalability, and resilience.
</li></ul>Governance, Risk, Compliance &amp; Ethics
<br>
<ul><li>Implement data governance, security, and privacy standards, including RBAC/ABAC and protection of PII and sensitive datasets.
</li><li>Establish and monitor automated controls for model drift, fairness, explainability, and ethical use of AI.
</li><li>Ensure compliance with relevant regulations (e.g., GDPR, HIPAA) and internal policies.
</li><li>Maintain best practices for data quality, versioning, reproducibility, and auditability.
</li></ul>Innovation &amp; Continuous Improvement
<br>
<ul><li>Lead experimentation with new algorithms, methods, and technologies to maintain a cutting edge AI ecosystem.
</li><li>Promote a culture of continuous improvement, refining processes, tools, and models to enhance performance and business impact.
</li></ul>Team Leadership &amp; Capability Building
<br>
<ul><li>Mentor and develop a high performing team of data scientists, AI engineers, and data professionals.
</li><li>Oversee training and upskilling initiatives to ensure the team remains current with emerging technologies and methodologies.
</li></ul>Stakeholder Management &amp; Communication
<br>
<ul><li>Serve as a strategic advisor between business and technical teams&mdash;translating business challenges into AI solutions and articulating AI outcomes clearly to non technical stakeholders.
</li><li>Provide regular updates to leadership on progress, performance metrics, model outcomes, and business results.
</li><li>Drive cross department collaboration to ensure successful adoption and integration of AI solutions.
</li></ul>Technology, Tools &amp; Vendor Management
<br>
<ul><li>Lead the selection, integration, and management of AI platforms, data tools, and vendor partnerships.
</li><li>Manage budgets for data science and AI initiatives, ensuring optimal allocation of resources.
</li><li>Make decisions related to hiring, vendor selection, and solutions procurement that support strategic AI objectives.</li></ul></div></div>
<br><div><div>Education &amp; Professional Qualification:
<br>
Bachelor&rsquo;s Degree in Computer Science, Data Science
<br>
Artificial Intelligence, Statistics, Mathematics, Engineering, Information Technology
<br>
Master&rsquo;s Degree (preferred):
<br>
Data Science, Artificial Intelligence or Machine Learning, Computer Science, Business Analytics, Statistics or Applied Mathematics, Operations Research
<br>
Professional Experience:
<br>
<ul><li>8 years of hands-on experience in data science, machine learning, or AI roles, including developing and deploying AI models and algorithms.
</li><li>Experience with machine learning techniques, such as:
</li><li>Supervised/Unsupervised Learning
</li><li>Deep Learning (e.g., Neural Networks, CNNs, RNNs)
</li><li>Natural Language Processing (NLP)
</li><li>Computer Vision
</li></ul>Geographic Experience:
<br>
A plus
<br>
Computer Skills:
<br>
data science, machine learning, AI, including developing and deploying AI models and algorithms
<br>
Language Skills:
<br>
Business fluent English
<br>
Arabic Language is an advantage.</div></div>

Specialist - AI & Data Architect

National Grid

<div><div><div><div><div>Team</div><div><b>
Data, Analytics and Insights</b></div><div>
Business Unit</div><div><b>
New England Jurisdiction</b></div><div>
Requisition No.</div><div><b>
7143</b></div><div><div>
City</div><div><b>
Waltham, US, 02451</b></div><div><b>
Hicksville, US, 11801</b></div><div><b>
Syracuse, US, 13202</b></div><div><b>
Brooklyn, US, 11217</b></div></div><div>
Date posted</div><div><b>
26 April 2026</b></div></div></div><p></p><div>
</div></div><div></div><div><h1 class="jobSectionHeader"><b>
Lead Data Scientist</b></h1><div></div><div><div><div><div><p><b><br>
National Grid is hiring a Lead Data Scientist (hybrid) in the following preferred locations: Brooklyn NY, Hicksville NY, Syracuse NY or Waltham MA.</b></p><p>
Every day, we deliver safe, reliable energy to homes, communities, and businesses. As our industry evolves, we&rsquo;re focused on meeting customer needs today while building more flexible, sustainable energy systems for tomorrow.</p><p>
To be successful, we must anticipate the needs of our customers, reducing the cost of energy delivery today and pioneering the flexible energy systems of tomorrow. This requires us to deliver on our promises and always look for new opportunities to grow, both ourselves and our business.</p></div></div><div><div><h3 class="jobSectionHeader"><b>
Job Purpose</b></h3></div><div><p>
The Customer Performance Lab supports the Customer organization by delivering cross‑channel and cross‑departmental insights that improve how customers move through our processes. We analyze operational, digital, and field interactions end‑to‑end to strengthen the customer journey and identify opportunities for continuous improvement across multiple portfolios.</p><p>
The successful candidate will leverage strong data processing, advanced SQL skills (including writing and optimizing stored procedures), requirements‑gathering capabilities, and analytical expertise to develop reporting, perform multi‑channel analysis, validate datasets, and build Power BI dashboards.</p><p>
This role collaborates closely with business stakeholders, IT partners, and other data teams to translate complex customer‑journey data into actionable insights and support key initiatives across Customer organization.</p></div></div><div><div><h3 class="jobSectionHeader"><b>
Key Accountabilities</b></h3></div><div><ul><li>
Develop, test, evaluate, tune, and deploy predictive and machine learning models</li><li>
Coordinate end to end algorithm/model development efforts among advanced analytics team members</li><li>
Advise senior leadership about the conclusions drawn from data science projects</li><li>
Educate team members and IT organization and business users about data science and approaches</li><li>
Work with data engineers to acquire, extract, transform, manage and manipulate data sets for specific use cases</li><li>
Create clear documentation of workflows, process flows, and methodologies used</li><li>
Agile delivery of data collection and analytics</li><li>
Gather requirements and documentation of the requirement specification / Power BI report development for data visualization</li><li>
Analytical, Investigative reasoning and problem-solving skills.</li></ul></div></div><div><div><h3 class="jobSectionHeader"><b>
Qualifications</b></h3></div><div><ul><li>
Advanced Degree in Analytics, Data Science, Economics, Statistics, Mathematics, Computer Science, or related field</li><li>
7+ years of experience in data analysis, data modeling, and working with large datasets or related experience</li><li>
Advanced SQL skills (including complex joins, window functions, stored procedures); proficiency in Python for data processing</li><li>
Strong experience building Power BI dashboards and connecting cross‑channel datasets</li><li>
Ability to conduct cross‑departmental analysis and translate business requirements into data solutions</li><li>
Experience with statistical methods or machine learning is a plus (not required)</li><li>
Utility industry experience strongly preferred</li></ul></div></div><div></div><div><div><p><br>
#LI-Hybrid</p><p>
#LI-STR1</p></div></div></div><p><b>
Upstate NY Salary: $114,000 - $133,000 a year</b></p><p><b>
MA Salary: $127,000 - $149,000 a year</b></p><p><b>
Downstate NY Salary: $136,000 - $160,000 a year</b></p><p>
National Grid utilizes an assessment that evaluates the job qualifications/characteristics using AI or statistically based scoring.</p><p>
This position has a career path which provides for advancement opportunities within and across bands as you develop and evolve in the position; gaining experience, expertise and acquiring and applying technical skills. Candidates will be assessed and provided offers against the minimum qualifications of this role and their individual experience.</p><p>
National Grid is committed to providing equal employment opportunities to all employees and applicants for employment regardless of protected class. We do not discriminate on the basis of race, color, religion, sex (including pregnancy, gender identity, and sexual orientation), national origin, age, disability, genetic information, or any other protected status in accordance with applicable federal, state, and local laws. National Grid maintains affirmative action programs for individuals with disabilities and protected veterans.</p><p>
Our employment practices are designed to ensure that all individuals are treated fairly and with respect throughout the hiring process and during employment. National Grid complies with all applicable federal, state, and local anti-discrimination laws. We are dedicated to fostering a workplace that is free from unlawful discrimination and harassment, and we encourage a culture of respect for all.</p></div></div></div><div></div>

Lead Data Scientist

Google

<div>In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:<br>
<ul><li>
Health, dental, vision, life, disability insurance</li><li>
Retirement Benefits: 401(k) with company match</li><li>
Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment</li><li>
Sick Time: 40 hours/year (increased to 69 hours/year for Seattle) including 5 discretionary sick days per instance
</li><li>Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks</li><li>
Baby Bonding Leave: 18 weeks</li><li>
Holidays: 13 paid days per year</li></ul><br>
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: <b>Seattle, WA, USA; Kirkland, WA, USA; New York, NY, USA; Sunnyvale, CA, USA</b>.<h3 class="jobSectionHeader"><b>
Minimum qualifications:</b></h3><ul>
<li>Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.</li>
<li>7 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.</li>
<li>Experience deploying ML models into production environments.</li></ul><br>
<h3 class="jobSectionHeader"><b>
Preferred qualifications:</b></h3><ul>
<li>9 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.<br>
</li>
<li>Experience with Machine Learning Operations (MLOps) tools and practices.</li>
<li>Understanding of business-to-business (B2B) enterprise SaaS business cycles, demand generation funnels, and marketing technology stacks.</li>
</ul><div><h2 class="jobSectionHeader"><b>About the job</b></h2><p>
Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.</p>
<p>
As a Staff Business Data Scientist, you will serve as a full-stack technical lead, owning the end-to-end life-cycle of data science products that drive Google Cloud&rsquo;s marketing and Go-to-market (GTM) strategy and measurement. You will move beyond traditional analysis to architect and build scalable intelligence systems.</p><p>
In this role, you will bridge the gap between data engineering and data science. You will build the infrastructure required to ingest and process massive datasets, develop predictive models (e.g., lead scoring, propensity predictions), and engineer the Application Programming Interfaces (APIs) or serving layers that integrate these insights directly into our marketing measurement and tech stack. You will have a specific mandate to leverage Google&rsquo;s Generative AI capabilities and will utilize Large Language Models (LLMs) and Gemini models to engineer novel data science products that enhance our predictive capabilities. You will advocate for software engineering best practices within the data science team, ensuring our code is testable and maintainable. You will work with Marketing leadership to ensure the intelligence systems you build actively influence decision-making. You will also mentor data scientists on the team and advocate for statistical methodology and coding standards across the organization.</p>
The US base salary range for this full-time position is $192,000-$278,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.<div></div><div><br>
Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.</div></div><div><h2 class="jobSectionHeader"><b>
Responsibilities</b></h2><ul>
<li>Lead the full Machine Learning (ML) life-cycle from data extraction and feature engineering to model training, validation, and deployment for critical marketing capabilities. Design and build ML models that solve ambiguous business problems and optimize the full customer life-cycle and demand funnel.</li>
<li>Design scalable data science applications using Google&rsquo;s LLM models to unlock insights from structured and unstructured data, build intelligent marketing agents, and automate decision-making processes within the Business-to-Business (B2B) funnel.</li>
<li>Define coding standards and engineering best practices for the team; mentor other data scientists on writing production-quality code and designing scalable architectures.</li>
<li>Partner with engineering and cross-functional data science teams to integrate model outputs directly into our martech systems, ensuring insights drive automated action.</li>
<li>Translate data science outputs into clear, actionable business recommendations for Director and Vice President level stakeholders.</li>
</ul></div>Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also Google's EEO Policy and EEO is the Law. If you have a disability or special need that requires accommodation, please let us know by completing our Accommodations for Applicants form.</div>

Staff Business Data Scientist, Google Cloud Marketing

mSupply

<div><div><b>About mSupply</b>
</div><div>mSupply is North America&rsquo;s leading distributor of OEM repair parts and equipment, serving professionals in the appliance, HVAC, plumbing, commercial kitchen and pool/spa industries. Headquartered in St. Louis, MO, mSupply is a multi-billion dollar enterprise offering an extensive product range, industry expertise and seamless service. With more than 2,000 associates across the U.S. and Canada, mSupply&rsquo;s family of brands delivers with speed, reliability and precision through its branches, distribution centers and extensive fleet of delivery vehicles. Shipped orders reach 93% of U.S. customers via next-day ground delivery and 100% within two days. For more information, visit mSupply.com.</div>
<div></div><div><b>The Principal Data Scientist</b> is responsible for the strategy, standards, and business impact of mSupply&rsquo;s data science function. This role directly manages Senior Data Scientists and an ML/AI Engineer, owns the AI roadmap, and is accountable for all modeling, experimentation, and AI capability delivered by the team. Use cases span demand forecasting, inventory optimization, pricing strategy, customer segmentation, and operational efficiency across plumbing, appliance, and HVAC distribution.</div>
<div></div><div><b>Job Duties &amp; Responsibilities</b></div>
<ul><li>Directly manage Senior Data Scientists and an ML/AI Engineer, setting expectations for quality, velocity, collaboration, and professional growth.</li>
<li>Own the data science and AI roadmap, sequencing initiatives based on business value, technical feasibility, data readiness, and team capacity.</li>
<li>Lead development of the most complex and strategically significant models, including enterprise forecasting frameworks, multi-variable optimization, and novel analytical approaches.</li>
<li>Define and evolve the team&rsquo;s operating model, including research-to-production handoff processes, peer review standards, and experiment design methodology.</li>
<li>Serve as the primary technical advisor to senior leadership on AI and advanced analytics strategy, communicating capabilities, limitations, risks, and investment tradeoffs.</li>
<li>Lead build vs. buy evaluations for AI capabilities, assessing vendor models, Azure AI services, and open-source options against custom development.</li>
<li>Establish the team&rsquo;s approach to model validation, bias assessment, responsible AI practices, experiment tracking, and model governance.</li>
<li>Design and standardize experimentation methodology including A/B tests, holdout evaluations, and model lift measurement frameworks.</li>
<li>Review and approve all models before production, applying a quality bar that balances statistical rigor with business pragmatism.</li>
</ul><div><b>Qualifications</b></div>
<div><b>Required</b></div>
<ul><li>Master&rsquo;s or Ph.D. in Data Science, Statistics, Computer Science, Mathematics, Operations Research, or a related quantitative field, or equivalent demonstrated experience.</li>
<li>8+ years of progressive experience in data science, machine learning, or applied AI, with at least 4 years leading or managing data science teams.</li>
<li>Expert proficiency in Python and modern ML libraries including scikit-learn, XGBoost, LightGBM, and at least one deep learning framework (PyTorch or TensorFlow).</li>
<li>Deep expertise in statistical modeling, forecasting, optimization, experimental design, causal inference, and Bayesian methods.</li>
<li>Demonstrated track record of taking models from research through production with measurable business impact.</li>
<li>Experience building and scaling data science teams, including hiring, mentoring, and establishing team operating models.</li>
<li>Strong executive communication skills with proven ability to influence senior stakeholders through data-driven insights.</li>
</ul><div><b>Preferred</b></div>
<ul><li>Experience designing or overseeing MLOps frameworks, production model governance, and responsible AI practices at enterprise scale.</li>
<li>Familiarity with Microsoft Fabric, Azure data services, Azure OpenAI Service, or dbt and the medallion architecture.</li>
<li>Background in wholesale distribution, supply chain, HVAC, plumbing, or related B2B sectors.</li>
<li>Experience with LLM-based workflows, RAG, agentic AI, or AI-assisted analytics.</li>
<li>Experience or demonstrated ability using AI-powered tools (e.g., code assistants, LLMs, AI-augmented workflows) to accelerate work and improve outcomes.</li>
</ul><div><b>Physical Demands &amp; Work Environment</b></div>
<div>This position may require over 40 hours per week and includes regular physical activity such as:</div>
<ul><li>Primarily sedentary work, requiring extended periods of sitting while working at a computer</li>
<li>Ability to communicate effectively in person, via phone, and through virtual collaboration tools</li>
<li>Occasional standing, walking, and movement during meetings, site visits, or travel</li>
<li>Ability to travel periodically for business needs, including visiting distribution centers, suppliers, stores, or industry events</li>
<li>Office environment includes standard business equipment such as computers, phones, and conferencing tools</li>
</ul><div><b>What We Offer:</b></div>
<div>We prioritize your well-being from day one with a comprehensive benefits package that includes:</div>
<ul><li>Medical, dental, vision, and prescription coverage effective immediately</li>
<li>401(k) plan with company contributions</li>
<li>Life insurance and short-term disability coverage</li>
<li>HSA/FSA options and an Employee Assistance Program (EAP)</li>
<li>Paid time off, including vacation, holidays, and personal days</li>
<li>Weekly pay, employee discounts, and more</li>
</ul><div><b>Equal Employment Opportunity &amp; Pre-Employment Requirements</b></div>
<div>mSupply is an Equal Opportunity Employer. We make employment decisions without regard to sex, age, race, color, creed, religion, national origin, citizenship or immigration status, sexual orientation, gender identity or expression, disability, genetic information, marital status, veteran or military status, or any other status protected by applicable federal, state, or local law.</div>
<div></div><div>We are committed to providing reasonable accommodations for qualified individuals with disabilities and to applicants with sincerely held religious beliefs. To request a reasonable accommodation, please contact careers@msupply.com.</div>
<div></div><div>Final offers of employment may be contingent upon completion of job-related pre-employment checks and screenings permitted by law. For roles that require operation of a company vehicle, a Motor Vehicle Record (MVR) check may also be conducted. This employer participates in E-Verify to confirm employment eligibility in the United States.</div></div>

Principal Data Scientist

MA'ADEN

<div><p><b>Job Purpose
</b></p><ul><li><div>The Lead Specialist, Data Science &amp; Analytics, acts as a technical leader and senior practitioner, driving development, deployment, and scaling of Machine Learning, AI, and advanced analytics solutions across Maaden.
</div></li><li><div>The role ensures analytics products are designed, validated, industrialized, governed, and adopted at scale, providing measurable value across mining, processing, operations, and enterprise functions.
</div></li><li><div>Lead Specialist, Data Science &amp; Analytics is to analyze data, extract insights, and build predictive models that help organizations make smarter decisions and solve difficult problems. By blending expertise in statistics, computer science, and business strategy, they not only analyze complex datasets but also build predictive models that improve operations and shape long-term decisions. With nearly every industry leaning on data today, the demand for skilled professionals continues to grow<br>
</div></li></ul><p></p><p><b>Key Accountabilities:
</b></p><p><b>1. Lead End-to-End Data Science Delivery
</b></p><ul><li>Developing, implementing and maintaining databases and data collection systems
</li><li>Own the full lifecycle of ML/AI initiatives - from problem framing, data exploration, feature engineering, model development, validation, and MLOps handover.
</li><li>Deliver scalable and production-grade models, ensuring alignment with enterprise data governance and AI standards.
</li><li>Performing statistical analysis to understand and interpret data insights
</li><li>Applying data mining techniques to identify patterns, trends, and relationships in large datasets
</li><li>Building predictive models and machine learning algorithms to forecast future outcomes
</li><li>Creating clear data visualizations and reports to communicate findings to stakeholders
</li><li>Working with cross-functional teams to understand business needs and provide data-driven solutions
</li><li>Design and maintain reliable data pipelines and models in partnership with data engineering to ensure data is accurate, timely, and trustworthy for downstream use
</li><li><div>Ensuring data security and compliance with relevant regulations
</div></li><li><div>Drive experimentation, model versioning, automated retraining, and continuous improvement.<br>
</div></li></ul><div></div><p><b>2. Translate Business Needs into AI/Analytics Solutions
</b></p><ul><li><div>Establish frameworks and operating models that make data science accessible, scalable, and embedded within business and technical functions
</div></li><li><div>Engage BU/domain stakeholders to identify value creation opportunities and convert them into actionable analytics use cases.
</div></li><li><div>Build value hypotheses, KPIs, success criteria, and solution roadmaps in collaboration with Data &amp; AI leadership and business teams.<br>
</div></li></ul><p></p><p><b>3. Industrialize AI/ML Models (ML Ops &amp; Architecture)
</b></p><ul><li>Partner with data engineering, data platforms, and cloud/OT architecture teams to embed models into enterprise systems and operational layers.
</li><li>Set standards for production deployment, testing, monitoring, drift handling, and lifecycle governance.
</li><li>Ensure seamless integration of predictive and optimization models into enterprise platforms, control systems, and digital twins
</li>Leverage machine learning, optimization, and computer vision as enabling tools for performance, reliability, and sustainability improvements<br>
<li>
</li></ul><p></p><p><b>4. Responsible AI, Quality &amp; Governance
</b></p><ul><li>Ensure compliance with Maaden&rsquo;s Responsible AI, data quality, and data governance frameworks.
</li><li>Promote reproducibility, documentation, lineage tracking, and auditability across all data science assets.
</li>Ensure transparency, explainability, and continuous model governance across production and enterprise environments<br>
<li>
</li></ul><p></p><p><b>5. Stakeholder Management &amp; Value Realization
</b></p><ul><li>Communicate insights, results, risks, and recommendations to decision-makers using compelling narratives and visualization.
</li>Track value realization, adoption metrics, and operational impact to ensure measurable benefit.<br>
<li>
</li></ul><p></p><p><b>Minimum Qualification, Experience and Core Competencies:
</b></p><p><b>Minimum Qualifications:
</b></p><ul><li><p>Bachelor&rsquo;s degree in computer science, Data Science, Engineering, Mathematics, Statistics, or related fields.<br>
</p></li></ul><p></p><p><b>Minimum Experience:<br>
</b><br>
<br>
</p><ul><li><div><b>Minimum Experience:
</b></div></li><li><div>8 &ndash; 10 years&rsquo; experience in Data Science / Advanced Analytics with industrial, mining, or heavy-asset environments preferred. Including at least 2 years leading or mentoring analytics professionals
</div></li><li><div>Proven ability to translate business problems into analytic approaches: define hypotheses, design analyses, and synthesize results into clear recommendations.
</div></li><li><div>Strong proficiency with modern ML frameworks and cloud platforms (TensorFlow, PyTorch , Azure, AWS) &ndash; Microsoft AI Factory
</div></li><li><div>Strong technical fluency with modern analytics stacks, data modeling, SQL, and experience partnering effectively with engineering teams.<br>
</div></li><li><br>
<b>Machine Learning &amp; Advanced Analytics
</b><ul><li>Hands-on experience developing and deploying <b>machine learning models </b>, including <b>time-series forecasting, predictive modeling, and optimization use cases
</b></li><li>Strong understanding of <b>model performance, validation, stability, and business impact
</b></li></ul></li><li><b>Generative AI &amp; AI Agents
</b><ul><li>Practical experience with <b>Generative AI solutions </b>, including <b>copilots, intelligent automation, and agent-based workflows
</b></li><li>Ability to embed GenAI capabilities into enterprise processes to improve decision-making and operational efficiency
<br>
<br>
Good to Have Capabilities
</li></ul></li><li><b>Data Engineering (IT + OT)
</b><ul><li>Experience designing and maintaining <b>data pipelines </b>across IT and OT environments
</li><li>Exposure to <b>sensor data, streaming / real-time data processing </b>, and industrial data sources
</li><li>Ability to collaborate with data engineering teams to ensure reliable, timely, and trusted data flows
</li></ul></li><li><b>MLOps / AgentOps
</b><ul><li>Experience in <b>model deployment and lifecycle management </b>, including:
<ul><li>Transition from model development to <b>production and scale
</b></li><li>Monitoring, retraining, versioning, and drift management
</li></ul></li><li>Familiarity with automation and operationalization of ML/AI workloads
<br>
<br>
Preferred Experience &amp; Platforms
</li></ul></li><li><b>Cloud &amp; Analytics Platforms
</b><ul><li>Experience working with enterprise cloud platforms, preferably:
<ul><li><b>Microsoft Azure Data Platform
</b></li><li><b>Databricks AI Platform
</b></li><li><b>Microsoft AI Foundry / Microsoft AI Factory
</b></li></ul></li>Understanding of cloud-native architectures for scalable analytics and AI solutions<br>
<li>
</li></ul></li></ul><div></div><p><b>Core Competencies:
</b></p><ul><li><p><b>Model Accuracy &amp; Reliability: </b>Performance, drift stability, and operational uptime.
</p></li><li><p><b>Adoption &amp; Business Impact: </b>Value realized, user adoption, integration success.
</p></li><li><p><b>Delivery Velocity: </b>Timeliness of development cycles and deployment readiness.
</p></li><li><p><b>Compliance &amp; Quality: </b>Alignment with Responsible AI, governance, and documentation standards.</p></li></ul></div><p></p>

Principal Specialist, Data Science & Analytics

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