Data Scientist

• Evaluate business objectives, collaborate in user research, understand process nuances, determine stakeholder needs, and identify requirements
• Champion the AI cause with stakeholder groups, educate and excite sponsors about possibilities offered by the data science discipline
• Choose best fit methods, define algorithms, validate, and deploy models to achieve business results
• Build AI solutions iteratively, in short cycles to enable a fail-fast, rapid learning environment
• Perform necessary data preparation and enhancements to models
• Interface directly with senior business leaders and stakeholders to architect and lead the development of advanced analytics solutions supporting Aircraft operations, Engineering, Supply Chain, Manufacturing, Quality, and Safety
• Work with data scientists leads across domains to ensure functional excellence in this technical field and will provide mentoring, technical leadership, and training to the data scientist community to accelerate and develop the talent base
• Provide thought leadership for machine learning and advanced analytics methods at Boeing and externally
• Engage with stakeholders across the business to identify applications across the enterprise, beyond the initial use case, and lead the implementation of these applications to capture business value
• Evaluate technologies, assist in identifying capability gaps, develop and teach best practices and technology standards, and work alongside the rest of the global data scientists and technical leads in Analytics organization
• Ensure analytics solutions meet the business requirements and are implemented in a timely manner to validate business value
• Work effectively with cross-functional teams in the analysis of highly complex data sets using advanced analytics techniques such as machine learning, advanced statistical analysis, visual analysis, text analysis, mathematical optimization, and simulation
• Identify modeling attributes and parameters
• Follow best practices and standard processes for model validation and refinement as per business requirements
• Assess performance of models and analysis and confirm that business objectives were met
• Work with business unit stakeholders to develop and sustain analytics solutions

Lead Artificial Intelligence/Machine Learning Data Scientist

• Lead brainstorming of new AI solutions
• Mentor data scientist community and provide technical leadership
• Develop and deploy AI and ML products
• Interface with senior leaders to architect advanced analytics solutions
• Work with cross-functional teams to analyze complex data sets
• Validate business value of analytics solutions

Lead Artificial Intelligence / Machine Learning Data Scientist

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
• Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
• Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
• Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
• Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
• Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B or uplift experiments to quantify impact and guide iteration.
• Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

Ads AI Analytics Lead

• Own clinical data quality across claims, EHR, and ADT: Define standards for how clinical data is structured, normalized, and validated as modeling inputs across payer claims (medical, pharmacy, eligibility), EHR data (Epic, Cerner, Athena), and real-time ADT feeds. Bring deep familiarity with EHR data formats (FHIR, HL7, C-CDA) and how data from systems like Epic, Cerner, and Athena maps to clinical reality. Hold the bar for clinical accuracy and completeness across all three sources.
• Build and ship production ML/AI models: Develop, validate, and deploy risk stratification, care gap prediction, treatment effect estimation, and LLM/foundation model applications — with rigor around leakage, calibration, fairness, and clinical face validity.
• Apply health economics and outcomes methods: Translate raw clinical and claims data into decision-grade evidence through risk adjustment, utilization measurement, cost attribution, quasi-experimental evaluation, and outcomes measurement aligned with CMS, NCQA, and MCO reporting standards.
• Advance machine and AI products: Bring senior modeling judgment to the product roadmap, owning the clinical and methodological soundness of what ships.
• Set standards and mentor: Make architectural trade-offs, drive alignment across data science, engineering, product, and clinical stakeholders, and mentor junior data scientists to raise the technical bar of the team.

Principal Data Scientist, Health Informatics

• Drive the roadmap and priorities for your team, and work with many Stripe leaders across the company to enhance our ability to be data-driven.
• Collaborate with stakeholders across the organization such as engineering, analytics, operations, finance, and marketing.
• Lead and manage processes to help the team do its best work and engage effectively with the rest of Stripe.
• Manage a high-performing team of data scientists, supporting them to achieve a high level of technical excellence and advance in their careers.
• Recruit and onboard great data scientists, in collaboration with Stripe's recruiting team.
• Contribute to broad data science initiatives as a member of Stripe's data science management team.

Data Science Manager – Finance and Strategy

• Collect, organize, and wrangle data from SAP and non‑SAP sources; build reusable analytics and data pipelines
• Clean, validate, and preprocess structured and unstructured datasets to ensure high‑quality inputs for analysis
• Conduct statistical and exploratory analyses to identify trends, patterns, and business insights
• Develop predictive and prescriptive models using SAP Analytics Cloud Predictive or external ML frameworks integrated with SAP data
• Enhance data collection procedures to support analytic system development
• Create dashboards, KPIs, and data visualizations to clearly communicate findings to business stakeholders
• Support data reconciliation and KPI validation between source systems and analytics outputs
• Collaborate with IT, business analysts, and leadership to implement data‑driven solutions
• Design and deliver AI/ML‑based decision‑making frameworks; measure and justify model value.

SAP Data Scientist

• Partner across Audit, Technology, and platform teams to build a unified Audit platform with reusable data, analytics, automation, GenAI services, model operations, secure delivery, and enterprise controls.
• Develop advanced analytics, AI, and data science solutions to solve complex business and technical challenges and shape technical direction.
• Design, test, and validate audit solutions using advanced data science methods aligned with audit standards and methodology.
• Apply data science to improve risk measurement, valuation, decision-making, and business performance.
• Create technical strategies and executive-ready materials that communicate high-impact solutions to leaders and stakeholders.
• Provide thought leadership on applying advanced analytics and data science to business challenges.
• Build solutions for continuous monitoring, risk detection, automated evidence generation, and deeper insights.
• Assess model effectiveness and fitness for use, ensure testing and monitoring, and explain key drivers and limitations.
• Lead cross-functional teams through the model lifecycle, aligning changes with business goals.
• Stay current on industry practices, regulations, and internal standards to ensure compliance and escalate issues as needed.
• Advise senior leaders on priorities, balancing accuracy, speed, cost, and governance.
• Support the Model Owner and Lead Model User in building consensus, prioritizing requirements, testing changes, resolving findings, and sharing best practices.
• Drive continuous improvement in modeling and analytics while promoting accountability, transparency, and proactive model risk management.
• Represent the Analytics team in internal forums, regulatory settings, and industry conferences, sharing best practices and thought leadership.

Data Science Advisor

• Partners with scientists, engineers, and community collaborators to curate and coordinate multimodal biomedical datasets.
• Guides researchers through data ingestion into data hubs.
• Supports the development of data models and annotations for specialized biomedical data types.
• Contributes to data governance and workflow design that enable discovery-driven science.
• Ensures shared datasets are usable, well-governed, and scientifically valuable.
• Adapts to evolving project needs and contributes to a culture of learning and collaboration.

Biomedical Data Manager

• Design and implement AI native applications and software products using GenAI coding tools.
• Design and implement advanced data visualization solutions to communicate complex insights effectively.
• Collaborate with stakeholders to understand business needs and translate them into data-driven strategies and actionable models.
• Lead the development and deployment of data analysis products, ensuring alignment with business objectives and scalability requirements.
• Define data governance standards and quality guidelines to support robust analytical modeling and data management.
• Mentor teams on best practices in analytics, visualization, validation and GenAI development to drive continuous improvement.
• Influence strategic decisions by integrating data insights with business context and measuring return on investment.

Senior Distinguished I, Data Scientist

• Partner with business, product and engineering teams to deeply understand the problem.
• Rapid prototype, design, implement, test, evaluate, deploy and improve software solutions that solves challenging business and technical problems.
• Use agentic coding tools, frameworks and techniques such as MCP, A2A, ADK, and frameworks to accelerate development.
• Improve outcomes through feedback-driven workflows using real-world signals.
• Contribute to multi-agent workflows with coordination, delegation, and dynamic task execution.
• Ensure operational excellence and build observability, debugging, and traceability in AI-driven systems.
• Lead the knowledge sharing and training of the rest of the organization on AI-native application development.

Staff Data Scientist

• Join Walmart as a Staff Data Scientist and leverage expertise in predictive analytics and machine learning.
• Identify and source relevant data from multiple business domains and databases to support analytics initiatives.
• Perform data quality assessments and ensure data integrity for accurate model development.
• Develop, validate, and tune predictive models using advanced statistical and machine learning techniques.
• Translate complex business problems into data-driven solutions aligned with strategic objectives.
• Create clear and impactful data visualizations to communicate insights to stakeholders.
• Collaborate with cross-functional teams to deploy and monitor models in production environments.
• Mentor junior team members on analytical methods and best practices.

• Partner with Marketing to Define and Solve Problems – Work closely with marketing stakeholders to understand business challenges, define success metrics, and translate needs into analytical approaches that drive performance across campaigns and channels.
• Design and Deliver Data-Driven Solutions – Apply statistical analysis and machine learning to develop solutions that address business needs, then present findings, influence decisions, and gain alignment on adoption.
• Lead Experimentation and Optimization – Develop and manage testing frameworks (A/B testing, campaign experimentation) across channels and markets. Analyze results and provide clear recommendations to improve performance and inform future strategy.
• Translate Results into Business Impact – Clearly communicate insights and quantify outcomes (e.g., campaign performance lift, engagement improvements, ROI) to ensure stakeholders understand the value and take action.
• Partner to Operationalize Solutions – Collaborate with Technology and Engineering teams to transition validated models into production, supporting implementation through scalable pipelines and processes.
• Influence Through Storytelling – Present insights and recommendations to both technical and non-technical audiences, including senior stakeholders, to drive alignment and decision-making.

Senior Data Scientist – Marketing Analytics

• Define and own the KPIs that measure CX health, downstream impact, product experience, and user sentiment.
• Analyze user behavior and marketplace dynamics to identify opportunities and inform CX product and agent team priorities.
• Measure the tradeoffs between user experience and business value.
• Translate complex data into actionable recommendations for product and leadership teams.
• Partner with product managers and engineers to design, implement, and evaluate A/B tests and feature rollouts.
• Develop frameworks for causal inference, impact measurement, and long-term ecosystem health.
• Build scalable methodologies to understand feature performance and guide iteration.
• Use our modern data stack to build dashboards, data pipelines, and self-serve tools that empower teams across Whatnot.
• Partner with engineers and operations leadership to improve data accessibility, ensure data quality, and support instrumentation for new product features.
• Advocate for data-driven decision-making and foster a culture of measurement across the CX organization.
• Communicate insights clearly to both technical and non-technical audiences, influencing roadmaps and strategic decisions.
• Serve as a thought partner to CX leads, shaping how we build, launch, and iterate on experiences across the platform.

Data Scientist, Customer Experience

• Implement industry-standard predictive modeling solutions
• Analyze, manipulate, clean, and process large and messy data
• Code original R/Python scripts and develop analytical reports
• Work independently or collaboratively on large data science problems
• Provide technical data science expertise to assist sales teams
• Conduct in-depth research to improve multi-source data quality

• Develop research methods and data analysis plans related to healthcare utilization, cost trends, financial forecasting, member, and provider experience.
• Expertly use appropriate software tools to retrieve, edit and tabulate data from various databases and files.
• Conduct descriptive, prescriptive, predictive analyses.
• Summarize complex data for leadership in the appropriate form (e.g., statistical summaries, narratives, charts, tables, graphs, etc.) to convey meaning, significance, and actionable information.
• Contribute/Lead in the research and development of models, algorithms, and analysis tools to mature the use and creation of self-service data products at CareOregon.
• Develop visual reporting (e.g., dashboards, scorecards, etc.) using data visualization tools such as Tableau when appropriate.
• Develop appropriate subject matter, domain expertise and serve as an expert resource.
• Collaborate with other subject matter experts to identify opportunities for building repeatable data products.
• Collaborate with business teams to understand business needs and to illicit and document requirements for analytics, algorithms, models, etc.
• Develop reporting tools to comply with regulatory reporting requirements.
• Review and assess completeness, validity, and reliability of internal data used for analysis and reporting.
• Create and maintain technical documentation such as data dictionaries, business glossaries, metric definitions, and other data resources.
• Thoroughly document models, algorithms, data products, processes and any analysis, reports developed.
• Contribute to, and actively participate in, community of practice to improve the skills and capabilities of other analysts at CareOregon.
• Assist with, prepare, and/or deliver presentations to community groups, regulatory agencies, leadership, and other teams as needed.

Data Science Analyst II

• Extract, prepare and model large, complex data sets using a combination of skills, including machine learning theory, mathematics, statistics, and programming.
• Deliver on-time quality analysis, interpretation, and synthesis of data into effective, concise, and actionable recommendations that enable intelligent decisioning for the company.
• Provide senior-level guidance and mentorship to the data science team, including reviewing projects, models, and code for peers and junior team members.
• Work with engineering teams to implement and improve machine learning pipelines and production-ready models.
• Effectively communicate important information and insights to business leaders using verbal, written, and data visualization skills.
• Also responsible for other Duties/Projects as assigned by business management as needed.

Senior Data Scientist

• Serve as the primary point of contact for At Work-related data and analytics needs
• Partner with CAPTRUST’s centralized data team to prioritize, design, and improve data solutions supporting At Work
• Help establish consistent definitions, metrics, and reporting standards across the At Work practice
• Improve data quality, clarity, and usability over time
• Support and enhance internal and client-facing reporting used by At Work teams
• Work closely with Operations to understand recurring reporting needs and reduce one-off or manual efforts
• Develop dashboards and reports that support operational planning, advisor effectiveness, and leadership decision-making
• Translate reporting outputs into insights that inform action, not just describe activity
• Develop qualitative and quantitative measures of client and participant engagement, trust, and relationship health
• Help establish ongoing feedback loops across the client and participant lifecycle, beyond episodic surveys
• Identify trends that signal strengthening relationships, stagnation, or potential breakdowns
• Support analysis related to the annual financial wellness survey and other experience-related initiatives
• Analyze patterns associated with strong long-term outcomes such as sustained engagement, retention, and expansion of advisory relationships as needs evolve
• Partner with leadership and Operations to identify where additional investment of time or resources may be needed
• Help reduce reliance on anecdotal feedback through clearer, shared insight

Data & Analytics Lead

• Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions.
• Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria.
• Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems.
• Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights.
• Develop and maintain model documentation, technical specifications, and implementation plans.
• Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence.
• Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions.
• Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact.
• Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics.
• Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk.
• Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability.
• Perform model validation, testing, and performance assessments prior to production deployment.
• Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives.
• Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes.
• Define key performance indicators and success metrics for machine learning and AI initiatives.
• Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods.
• Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks.
• Use experimentation and data-driven insights to guide product, operational, and strategic decisions.
• Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments.
• Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions.
• Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs.
• Support the creation of scalable, maintainable, and reliable AI and machine learning solutions.
• Ensure model deployment processes align with engineering best practices and operational requirements.
• Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions.
• Communicate complex analytical findings and technical concepts to both technical and non-technical audiences.
• Present recommendations, insights, and model performance results to leadership and project teams.
• Support technical reviews, project planning, and delivery activities across cross-functional initiatives.
• Contribute to knowledge sharing, documentation, and best practices within the data science organization.
• Provide technical guidance and mentorship to junior team members and peers as needed.

• Rigorously investigating, evaluating, interpreting, and delivering data-driven findings from relevant and diverse data sources.
• Preparing and delivering timely and actionable tactical analysis products tailored to the needs of the customer mission.
• Transforming and analyzing raw data into actionable findings, and communicating through charts, maps, timelines, and infographics to visualize complex relationships and big data sets in a user-friendly and easy-to-understand format to support operational decision making or criminal prosecution.
• Learning, applying, and maintaining relevant subject matter expertise in one or more technical areas in support of customer missions, through analytic work, operational tactics, data handling, methodology development, command briefings, and training and consultation.
• Rapidly responding to changing circumstances requiring flexibility, time-management, and the ability to function under shifting priorities while keeping track of ongoing requirements and deadlines.
• Communicating and collaborating on analysis with both internal and external analytic colleagues and with operational customers, in person or remotely, and interacting effectively with command-level and senior leadership.

• Setting the technical direction and measurement strategy for the shopping funnel
• Developing and mentoring data scientists across experimentation, causal inference, and product analytics
• Defining scalable frameworks for A/B testing, funnel optimization, and consumer behavior modeling
• Influencing senior product and business stakeholders with data-driven recommendations
• Ensuring analytical rigor translates into tangible product improvements and business growth
• Driving deep-dive analyses of renter behavior, market dynamics, and funnel performance to identify high-impact growth opportunities
• Managing and developing a team of data scientists working across shopping funnel measurement and consumer analytics
• Raising the bar on production-grade analytics and experimentation
• Operating as a strategic thought partner to senior stakeholders across Product, Engineering, and Business Strategy
• Leading thoughtful adoption of AI within your team.

See all jobs on JobTailor

Lead Artificial Intelligence/Machine Learning Data Scientist

Lead Artificial Intelligence / Machine Learning Data Scientist

Ads AI Analytics Lead

Principal Data Scientist, Health Informatics

Data Science Manager – Finance and Strategy

SAP Data Scientist

Data Science Advisor

Biomedical Data Manager

Senior Distinguished I, Data Scientist

Staff Data Scientist

Staff Data Scientist

Senior Data Scientist – Marketing Analytics

Data Scientist, Customer Experience

Data Scientist

Data Science Analyst II

Senior Data Scientist

Data & Analytics Lead

Senior Data Scientist

Senior Data Scientist

Manager, Data Science