Data Scientist

• Establish and scale a centralized customer analytics and intelligence function, defining standards, ways of working, and governance to ensure consistency, quality, and scalability.
• Design the foundational capabilities, team structure, and operating model needed to support evolving business needs.
• Partner closely with marketing, distribution, and leadership to embed analytics into planning, prioritization, and decision-making, ensuring insights drive measurable business outcomes.
• Lead conversations and presentations with C-level executives.
• Oversee insights collection, analysis, and dissemination across regions and business units.
• Ensure data integrity, accuracy, and relevance in reporting.
• Translate complex data into clear, actionable insights for leadership and stakeholders.
• Support strategic decision-making with timely and relevant insights.
• Drive continuous improvement and innovation in analytics processes.
• Facilitate cross-functional collaboration to leverage data and insights.
• Develop and maintain a robust insight reporting framework.
• Identify emerging trends, opportunities, and risks through proactive analysis.
• Translate marketing, advertising and CX goals into actionable data strategies, advising on segmentation, personalization, and activation to improve targeting, ROI measurement, and customer experience.
• Enable sales with quality leads and distribution clarity within CRM.

Customer Data & Analytics Lead

• Collaborate with a cross-functional team of product managers, researchers, designers and engineers to develop and iterate on product features and strategy
• Translate product objectives to data science problems and vice versa
• Apply statistical inference to draw rigorous conclusions from data, especially through designing and analyzing A/B tests and working on strategic insights that drive product strategy
• Use a combination of exploratory analysis and data mining techniques to generate actionable insights about user experience and user problems
• Collaborate with AI team to develop, iterate, and test production models (e.g., targeting)

Senior Data Scientist, Matching

• Maintain data pipelines for automated data processing suitable for downstream reporting, tabling, and summarizing
• Execute and build upon reproducible reports via efficient code for complex tabling and data visualization.
• Contribute to internal or external R packages.
• Collaborate with Clinical Operations and Data Management teams to understand emerging trial and business needs, assess available data, and determine how to appropriately summarize and present results.

Data Scientist I

• Own recruiting analytics end-to-end
• Build headcount + velocity models
• Make talent data decision-grade
• Bring signal to pipeline leadership reviews
• Improve the recruiting system
• Use AI as leverage in your first 90 days

People Analytics Lead – Recruiting

• Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal insights hidden within huge volumes of data
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation

Manager, Data Scientist – Card Intelligence Model Risk Management

• Partner with a cross-functional team of data scientists, software engineers, and product managers to deliver a product customers love
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
• Build machine learning models through all phases of development, from design through training, evaluation, validation, and implementation
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals

Manager, Data Scientist – Partnerships Acquisitions

Principal Associate, Data Scientist – Partnerships Acquisitions

• Partner with a cross-functional team of data scientists, software engineers, and product managers
• Leverage a broad stack of technologies - Python, Conda, AWS, H2O, Spark, and more
• Build machine learning models through all phases of development
• Translate the complexity of your work into tangible business goals

Manager, Data Scientist – Bureau Data Strategy

• Identify business challenges and opportunities for improvement and solve for them using analysis
• Perform modeling/analytics to assist new product and pricing strategies for various lending products
• Support direct-to-consumer marketing efforts for select products
• Support step-change improvements in credit performance
• Involve problem frame-works, develop hypotheses, test and analysis
• Work closely with colleagues across Capital One including: IT, Sales, Operations, Finance and others

Senior Business Analyst, Commercial Treasury Management Product Analytics

• Partner with a cross-functional team of data scientists, software engineers, and product managers to identify and quantify risks associated with models
• Leverage a broad stack of technologies — Python, Conda, AWS, H2O, Spark, and more — to reveal the insights hidden within huge volumes of numeric and textual data
• Lead teams of data scientists who build machine learning models to challenge current champion models
• Lead validation of a wide variety of models across multiple business domains

Principal Associate, Data Science – Model Risk Office

• Partner with a cross-functional team of data scientists, applied researchers, software engineers, machine learning engineers and product managers to deliver AI powered products that change how customers interact with their money
• Leverage a broad stack of technologies — Pytorch, Hugging Face, AWS Ultraclusters, LangChain, VectorDBs, and more
• Be the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs)
• Build NLP models through all phases of development, from design through training, evaluation, and validation

Principal Associate, Data Scientist – LLM Customization Team

• Partner with a cross-functional team of data scientists, software engineers, machine learning engineers and product managers to deliver AI powered products that change how customers interact with their money.
• Leverage a broad stack of technologies — Pytorch, AWS Ultraclusters, Hugging Face, LangChain, Lightning, VectorDBs, and more to reveal the insights hidden within huge volumes of numeric and textual data.
• Be the expert in Natural Language Processing (NLP) to harness the power of Large Language Models (LLMs), adapt and finetune them for customer facing applications and features.
• Build machine learning and NLP models through all phases of development, from design through training, evaluation, and validation; partnering with engineering teams to operationalize them in scalable and resilient production systems that serve 80+ million customers.
• Flex your interpersonal skills to translate the complexity of your work into tangible business goals.

Senior Manager, Data Science – AI Foundations

Principal Associate, Data Scientist – Cash Flow Underwriting

• Act as a core analytics partner to Podcast & Video content teams, providing deep-dive research and insights that shape long-term, data-driven programming and product roadmaps.
• Identify emerging trends in podcast listening and video consumption, pinpointing opportunities in priority markets to keep Spotify’s Podcast & Video strategy ahead of the curve.
• Transform complex data into scalable, actionable playbooks that empower global content and product teams to execute high-impact strategies.
• Design measurement frameworks using causal inference, experimentation, and modelling to quantify the incremental impact of Podcast & Video content and product decisions.
• Build and iterate on AI-powered dashboards and data visualisations that make Podcast & Video data more accessible and intuitive for stakeholders across the business.
• Leverage AI tooling, including LLMs and agentic coding assistants, to automate analytical workflows, accelerate insight generation, and explore new frontiers in data storytelling.
• Synthesise data findings into compelling narratives that drive alignment across Product, Engineering, and Content stakeholders.
• Gather stakeholder input to understand and translate their needs into clear data requirements and deliverables, supporting documentation, and knowledge sharing across the team.

• Own the end-to-end data science lifecycle for moderately complex models and significant project components — spanning data ingestion, feature engineering, modeling, validation, deployment, monitoring, and retraining.
• Apply expertise across several core areas of machine learning and statistics (e.g., gradient-boosted models, deep neural networks, time series, causal inference concepts, experimentation design), selecting appropriate methods for complex data science problems.
• Write efficient, modular, well-tested code for data processing, feature engineering, and model training/inference, leveraging distributed tooling (e.g., Vertex AI pipelines, Dataflow, BigQuery) where appropriate.
• Design and implement robust validation frameworks for complex experiments and models, accounting for potential biases and real-world performance.
• Troubleshoot complex model performance issues, data anomalies, and code bugs effectively with little guidance.
• Define analytical approaches and scope data science projects for moderately complex or ambiguous business problems.
• Partner with product managers and stakeholders to define success metrics and experiment goals, and to translate marketplace problems into data science solutions.
• Lead the design and analysis of experiments (e.g., A/B tests, switchback) for your projects, and interpret complex model results and experimental outcomes with a focus on actionable insights and business outcomes.
• Proactively identify opportunities within your domain where data science can provide significant value, and initiate exploration.
• Follow and help improve established team processes for coding standards, documentation, reproducibility, and experimentation.
• Mentor DS I and DS II scientists, providing technical guidance, reviewing code, analyses, and models, and supporting their growth in analytical and modeling skills.
• Influence technical decisions within the team regarding modeling choices, validation strategies, and tooling through well-reasoned arguments and expertise.
• Drive improvements to team standards, data science best practices, and analytical rigor; take ownership of specific team practices or technical components (e.g., a feature store component, leading experimentation reviews).
• Educate stakeholders on the capabilities and limitations of data science models, and clearly explain complex methodologies and findings to both technical and non-technical audiences.
• Participate actively in recruiting, providing high-quality, graded interview feedback for candidates up to this level.

Data Scientist III

• 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

• Own and execute data science work across autonomy, safety, and product, focusing on the highest-impact analytical problems
• Analyze machine telemetry, operational data, and user workflows to surface insights that improve system performance, safety, and usability
• Define and track key metrics that measure system behavior and product outcomes
• Design and evaluate experiments, analyses, and validation approaches in partnership with engineering and product teams
• Collaborate closely with ML platform and data engineering teams to ensure data is accessible, reliable, and fit for analysis
• Communicate findings clearly to technical and non-technical stakeholders, influencing roadmap and design decisions
• Contribute to the foundation of data science practices at Bedrock, helping shape how future work and teams scale

• Develop a deep understanding of Campus’ student-facing teams’ impact on enrollment, retention, and academic outcomes - from admissions and financial aid to coaching and tutoring teams
• Build a comprehensive measurement framework for student-facing teams’ effectiveness and efficiency
• Identify and execute top opportunities to improve student outcomes using causal inference and experimentation
• Collaborate with Product and Operations on integrating behavioral signals and predictive modeling outcomes’ into team workflows
• Partner cross-functionally with Product, Operations, and Engineering to optimize student impact staff journey at Campus - from hiring and training to software tooling and automation

Staff Data Scientist

• Designing and implementing AI systems to transform raw data into actionable insights
• Leading teams in the development of scalable machine learning models and solutions
• Managing complex data analysis and integration to support AI-driven initiatives
• Utilizing programming languages such as Python and Java to enhance AI model deployment
• Overseeing the creation and maintenance of data pipelines and infrastructure
• Applying deep learning techniques and neural networks to improve predictive analytics
• Collaborating with stakeholders to address data challenges and optimize AI applications
• Mentoring team members to develop skills in AI implementation and data engineering
• Validating data quality and compliance within AI frameworks
• Encouraging innovation and embracing change to drive business growth through AI solutions

See all jobs on JobTailor

Customer Data & Analytics Lead

Senior Data Scientist, Matching

Data Scientist I

People Analytics Lead – Recruiting

Manager, Data Scientist – Card Intelligence Model Risk Management

Manager, Data Scientist – Partnerships Acquisitions

Principal Associate, Data Scientist – Partnerships Acquisitions

Manager, Data Scientist – Bureau Data Strategy

Senior Business Analyst, Commercial Treasury Management Product Analytics

Principal Associate, Data Science – Model Risk Office

Principal Associate, Data Scientist – LLM Customization Team

Senior Manager, Data Science – AI Foundations

Principal Associate, Data Scientist – Cash Flow Underwriting

Data Scientist

Data Scientist III

Ads AI Analytics Lead

Principal Data Scientist, Health Informatics

Data Scientist

Staff Data Scientist

AI & GenAI Data Scientist – Manager