Data Scientist

• Design and deploy machine learning, NLP, and generative AI solutions that help researchers discover, understand, and apply scientific knowledge.
• Build intelligent retrieval, search, recommendation, ranking, and question-answering systems that improve research outcomes.
• Develop AI systems that connect information across publications, datasets, citations, knowledge graphs, and scientific ontologies.
• Fine-tune, evaluate, and integrate large language models and retrieval-augmented generation (RAG) systems into production environments.
• Create robust evaluation frameworks that measure quality, reliability, relevance, trustworthiness, and user impact.
• Build scalable data pipelines and machine learning workflows that support experimentation, monitoring, and continuous improvement.
• Apply the appropriate combination of classical machine learning, deep learning, retrieval, and generative AI techniques to solve complex scientific problems.
• Collaborate with engineering, product, UX, analytics, and domain experts to transform ambiguous challenges into practical solutions.
• Contribute clean, maintainable, production-quality Python code and reusable AI components.
• Continuously improve the capabilities, performance, and real-world value of AI systems that support scientific discovery.

• Lead and develop high-performing teams of data scientists, machine learning engineers, researchers, and technical contributors.
• Define and execute data science strategies that advance scientific discovery, research intelligence, and knowledge-access products.
• Drive the development of AI-powered capabilities across search, retrieval, recommendation, NLP, knowledge systems, and generative AI.
• Translate complex customer, scientific, and business challenges into scalable data science solutions and measurable outcomes.
• Establish high standards for experimentation, evaluation, model quality, reliability, and responsible AI practices.
• Partner closely with Product, Engineering, Research, UX, Analytics, and domain experts to shape product strategy and delivery.
• Mentor and coach team members while fostering a culture of scientific rigor, collaboration, innovation, and continuous learning.
• Guide the adoption of emerging AI technologies, including LLMs, retrieval-augmented generation, semantic search, and knowledge-based systems.
• Influence senior stakeholders and contribute to long-term AI, technology, and product strategy across the organization.
• Ensure that AI systems are trustworthy, scalable, explainable, measurable, and aligned with meaningful customer and societal outcomes.

Data Science Manager

• Take ownership of the design and execution of data science projects, ensuring high-impact analytics solutions.
• Partner with client stakeholders to understand business challenges and develop data-driven strategies.
• Oversee data acquisition and integration, ensuring data quality and reliability.
• Apply and refine machine learning and predictive modeling techniques to solve complex business problems.
• Guide the deployment and integration of predictive models into business applications.
• Develop and implement strategies for optimizing model performance and monitoring over time.
• Present findings and recommendations to executives, translating complex data concepts into actionable business strategies.
• Mentor and support junior and senior team members in data science methodologies and best practices.
• Influence data governance and best practices within the organization.

Senior Consultant, Data Scientist

• 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

• Own enterprise data science strategy and multi-year roadmap.
• Prioritize high-impact use cases with executive stakeholders.
• Oversee model lifecycle, from discovery to productionization.
• Establish enterprise level standards for MLOps and responsible AI governance.
• Develop talent, structure teams, and manage budgets.
• Communicate results and risks to senior leadership.

Senior Director, Data Science

• 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

• Responsible for developing and deploying CECL predictive models, advanced analytics, and advanced statistical techniques, solutions and actionable insights that deliver business impact.
• Develops strategic priorities, shapes analytical approaches, ensures model quality and performance and ensures alignment with corporate initiatives and goals.
• Manage daily activities of professional employees.
• Work is performed under limited supervision.

Manager II, Data Science – CECL Modeling

• 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

• 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

• 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

• Lead and manage a team of technical staff responsible for developing reusable data products such as dashboards, automations and Agentic AI capabilities to increase audit efficiency and effectiveness.
• Maintain Internal Audit’s data product backlog, including the intake, refinement, and prioritization of development in alignment with Internal Audit’s organizational priorities.
• Oversee the use of automation to reduce manual effort across the audit lifecycle, from pre-planning and planning through fieldwork, issues management and reporting while ensuring audit work meets quality standards, is well‑supported, and adheres to professional audit guidance (e.g., IIA Standards).
• Partner with senior Internal Audit leadership and cross‑functional teams (e.g., Model Risk Management, Enterprise Data Risk Governance) to implement appropriate frameworks and controls to manage AI and Agentic AI risk.
• Provide expert review of technical work products including code, requirements documentation, UAT results and project plans to ensure accuracy, completeness, and reliability of data products.
• Drive the evolution of our data science practices by implementing best practices in model development, validation, and deployment across various business units.
• Collaborate with senior leadership and cross-functional teams to define strategic objectives, establish key performance indicators (KPIs), and prioritize projects that deliver actionable insights to the organization.
• Communicate complex data-driven insights in a clear and concise manner to diverse audiences, fostering understanding and buy-in from stakeholders at all levels.
• Manage multiple teams and/or specialized units to include resource planning, ensuring the execution of complex modeling initiatives; partner with leaders in analytics, engineering, and business domains to integrate models into workflows.
• Analyze large complex datasets to extract actionable insights and present findings to stakeholders, using effective visualization and storytelling techniques.
• Review complex code and analyses to ensure accuracy, scalability, and ethical alignment.
• Ensure the continuous improvement of data quality and governance processes by collaborating with data engineering and IT teams.
• Ensure appropriate use of advanced statistical techniques and manage model lifecycle governance.
• Develop and manage budgets related to initiatives, ensuring effective resource utilization and alignment with organizational goals.
• Coach teams on cutting-edge data science methods and foster a culture of experimentation and curiosity.
• Research, evaluate, and integrate new data science technologies and methodologies to enhance the team's capabilities and drive innovation.

Manager II, Data Science – Audit Product Engineering

• This Data Science Manager Senior role focuses on delivering decision ready insight through Descriptive (“what happened?”) and Diagnostic (“why did it happen?”) Analytics, with concrete outputs such as executive dashboards, KPI frameworks, trend and cohort analyses, drill downs, segmentation, driver analysis, and root cause investigations.
• The role is accountable for translating complex data into clear performance narratives by defining standardized metrics, ensuring data quality and lineage, and embedding analyses into scalable BI assets rather than ad hoc reporting.
• As needed, the role performs Prescriptive Analytics (“what should we do?”) by turning findings into decision recommendations through scenario analysis, sensitivity testing, and practical optimization or decision models, and by operationalizing insights into dashboards, alerts, and playbooks.
• Predictive Analytics (“what will happen?”) is in scope but not required, with forecasting, risk or propensity scoring, and applied machine learning viewed as nice to have capabilities.
• The manager leads and prioritizes an analytics roadmap, coaches analysts and data scientists, and promotes disciplined use of tools such as SQL, Python, modern BI platforms, cloud data technologies, Git, and applied statistics.
• Strong partnership with product, operations, risk, and business leaders is essential to frame the right questions and deliver executive ready storytelling that connects analysis to action.
• The role enforces data governance, documentation, and audit ready practices, ensuring metric consistency and trusted reporting across teams.
• An experiment and measurement mindset is expected, applying test and learn or A/B testing to evaluate impact. Any models or advanced techniques used are monitored for performance, reliability, and fairness, with assumptions and limitations communicated transparently.

Senior Data Science Manager – Data, Modeling & Analytics

• Provide senior analytical leadership to optimize the bank’s centralized marketing decisioning engine and drive customer and business outcomes.
• Focus on improving the quality, performance, and stability of self learning models by developing analyses and business cases while partnering closely with data science teams and key business stakeholders.
• Shape and govern decision inputs, including algorithm refinement, value frameworks, propensity models, constraints, and business rules, while incorporating enterprise and channel context and ensuring regulatory compliance.
• Serve as mentor to more junior employees.
• Consult with clients and mentor consultants on tool and strategy implementation and monitoring, statistical scoring, business intelligence, data quality, and analytical product / solution development.
• Determine the optimal analytic approach and support development, implementation and enhancements.
• Analyze results and make recommendations for key business partners and senior management or communicate conclusions from complex analytical solutions to a wide range of audiences.

See all jobs on JobTailor