Top Senior Data Science Jobs in Hampton, VA

Build, deploy, monitor, and document predictive models and analytical reports; translate findings for non-technical stakeholders; ensure model quality, fairness, and explainability; collaborate with data engineers and train staff on data literacy; support production ML infrastructure on AWS.

Design and validate clinical and biomarker data pipelines, perform source-to-target mappings (e.g., EHR to data model), extend ontologies and vocabularies, ensure data quality, evaluate informatics tools, and support data architecture and analysis workflows for a regulatory-grade platform.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

Develop, implement, and maintain ALM and CCAR/PPNR models for balance sheet forecasting. Build customer behavior, prepayment, and deposit models; monitor performance, perform backtesting and sensitivity analysis; collaborate with Model Risk, Market Risk, Treasury, and validation teams; prepare documentation and materials for governance and senior leadership; support balance sheet optimization and regulatory initiatives.

The Senior Data Scientist will analyze customer data, design experiments, build models, and support data-driven decision-making across product, marketing, and customer experience teams.

Lead retention and churn efforts by building survival and lifecycle-state models, classifying churn reasons, creating personalized win-back and next-best-action interventions, optimizing offers/promos, designing uplift experiments, and owning production model lifecycle, monitoring, and MLOps across Product, CRM, Marketing, and Engineering.

The Principal Data Scientist will design, develop, and deploy machine learning models, innovating methodologies and collaborating across teams to enhance financial decision-making and risk management.

The Senior Data Scientist leads data-driven initiatives, optimizing analytics for business decisions, mentoring staff, and ensuring data quality across healthcare projects.

As a Senior Data Scientist, you will identify business opportunities, build machine learning solutions, partner cross-functionally, and translate analyses into actionable insights.

Lead development of graph-based identity resolution for CTV advertising: design models and validation, analyze large identity datasets with distributed tools, productionize data-science workflows, partner with engineering/product, and apply privacy-by-design while leveraging LLMs and AI-assisted development.

Design, validate, and deploy predictive models using claims, clinical, pharmacy, and lab data to improve population health, utilization management, and care quality. Partner with clinical and business teams, evaluate model performance, scale analytical approaches, and communicate insights to technical and non-technical stakeholders.

Lead the intelligence layer atop the data lakehouse: design and run causal experimentation, build predictive and forecasting models, own model evaluation and monitoring, deploy and maintain models on Databricks/MLflow, set technical direction for data science, and partner closely with data engineers, AI engineers, and product. Use AI development tools daily.

Lead data science strategy across complex customer engagements, turn ambiguous business problems into repeatable ML solutions, define modeling and evaluation standards, drive data readiness and MLOps practices, mentor teams, produce reusable IP, and represent Microsoft in executive and thought-leadership forums.

Partner with business, finance, and engineering teams to design and evaluate incentive compensation plans. Build scalable data pipelines, ML models, NLP and AI agents, and dashboards to generate actionable insights, perform scenario modeling, and ensure quota and compensation outcomes are explainable, reliable, and aligned to business goals.

Lead development and deployment of ML and AI solutions for global incentive and quota design. Partner cross-functionally to define quota strategy, build scalable data pipelines and models, create AI agents, evaluate model performance, ensure governance/compliance, and communicate insights to stakeholders to drive business outcomes.

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

As a Pricing Data Scientist, you'll develop and validate pricing models for alternative assets, collaborate with engineers, and enhance valuation accuracy through data insights.

Develop and validate SDTM and ADaM datasets using R following CDISC standards, support TLF generation in R/SAS, perform QC and reconcile data issues, collaborate with statisticians and clinical teams, document workflows, and support automation of R-based pipelines for clinical trial deliverables.

Lead customer-facing data science engagements: manage data acquisition/cleaning, develop and evaluate scalable models, drive modeling best practices, contribute thought leadership, write extensible code, and define solution and business strategy with cross-functional teams.

Lead customer-facing data science engagements: acquire and prepare data, evaluate and operationalize models at scale, drive modeling best practices, research industry trends, write extensible code, and define solution and customer strategy while promoting ethics and privacy.

Lead development and deployment of machine learning models, perform EDA and data engineering on large datasets, train models including LLMs and unstructured-data solutions, build Power BI dashboards, automate processes, present findings to leadership, and mentor junior data scientists while working with SQL, Python, AWS/Azure, Snowflake, and Salesforce.

Lead end-to-end delivery of strategic data science and AI projects: define business problems with stakeholders, prepare large datasets, build and deploy predictive, prescriptive, and generative AI models (including LLMs), implement prompt engineering and fine-tuning, ensure production-grade code and ML lifecycle practices, present insights to senior stakeholders, and promote responsible AI and adoption across teams.

Lead product and program analytics for Microsoft skilling experiences. Design funnel and learner-journey analyses, define success metrics, build measurement frameworks, run experiments and causal analyses, develop competency/skill models, partner with product and engineering on telemetry, recommend content and curriculum improvements, mentor data scientists, and ensure data privacy and telemetry quality.

Lead customer-facing data science engagements: manage data acquisition and cleaning, build and evaluate scalable models, drive model best practices and ethics, write extensible code, research industry trends, define solution strategy, and collaborate across engineering and product teams to deliver AI/cloud solutions.

Lead cross-functional teams to identify and implement revenue-driving strategies. Own revenue optimization, develop quantitative analyses (A/B testing, causal impact), improve data pipelines and tracking, embed AI into workflows, and present insights to leadership to scale and recover revenue.