Top Senior Machine Learning Jobs in Hampton, VA

Lead end-to-end healthcare fraud investigations for Clover's Medicare Advantage business. Own caseloads, gather and analyze claims and medical evidence, coordinate legal support, prepare defensible referral packages, leverage AI and analytics tools, and collaborate cross-functionally to drive recoveries and provider actions.

Lead architecture and implementation of high-performance software to deploy and operate AI/ML models for network threat detection. Build autonomous AI agent workflows, production ML pipelines, and services; drive cross-team engineering standards, mentor engineers, and represent technical direction to leadership.

The AVP, Digital Portfolio Management oversees P&L for small business products, driving underwriting profitability and digital transformation through analytics, cross-functional collaboration, and strategic initiatives.

Seeking a Senior Full Stack .NET Developer to work on a social good project, with strong skills in C#, AngularJS, design, and UX. Requires 5+ years of experience.

The Senior Data Scientist will design and deploy time-series forecasting models, collaborate with stakeholders, and operationalize models in cloud environments, focusing on demand planning across various industries.

Lead manufacturing process improvements for precision optical production: analyze data, reduce cycle time and costs, design/qualify tooling and automation, apply Lean/Six Sigma and AI-enabled analytics, maintain process documentation, partner cross-functionally, drive CAPEX and continuous improvement projects, and train production staff.

The Engineering Manager will lead teams in developing and managing Blackbaud's Data & AI platform, focusing on scalability, security, and cost-efficiency.

Lead a multidisciplinary team to design, deploy, and optimize ML-driven bidding systems and measurement for a nine-figure marketing budget. Drive experimentation, partner strategy, media-mix optimization, and end-to-end measurement while aligning Marketing, Analytics, Engineering, and Product to improve marketing ROI and scale performance.

Serve as the technical lead in pre-sales for state, local, and education customers: run discovery, design POCs and workshops, deliver demos, map needs to Socure solutions, advise on fraud/risk/compliance, influence product, and mentor junior consultants.

The role involves developing and integrating AI features into Cribl's products, collaborating with teams, and maintaining operational efficiency.

The Senior Machine Learning Engineer will design, implement, and optimize machine learning models, drive AI initiatives, and collaborate cross-functionally to enhance SailPoint's AI capabilities.

Lead quantitative credit analytics for a Send Now Pay Later (SNPL) product: build and maintain credit policies, pricing and NPV models, vintage analysis, portfolio monitoring, model integration/testing, and alternative data evaluation to optimize approval rates and manage portfolio health.

Lead design and scaling of a unified fraud and risk platform. Own roadmap, fraud metrics, SQL-based analysis, A/B testing, real-time detection models, and cross-functional delivery to balance customer experience and risk mitigation.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead fraud detection and mitigation for a conversational Send Now Pay Later product: define first-party fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, integrate ML and decisioning tools (Taktile), and coordinate SARs and compliance reporting.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, pricing models, vintage and NPV analyses, executive reporting, model integration and testing, and explore alternative data to optimize approval rates and portfolio health.

Lead credit analytics for the Send Now Pay Later product: build and optimize valuation, vintage and NPV models; manage credit policies, portfolio monitoring, and NPL reporting; integrate and test scoring models with engineering/data science; and research alternative data to improve predictive performance.

Lead fraud detection and mitigation for a conversational BNPL product: define fraud typologies, analyze repayment/chargeback trends, own fraud KPIs, design policies, collaborate on ML models and decisioning workflows, and coordinate SARs with Compliance.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

The role oversees analytical characterization and comparability studies for biologics, ensuring regulatory compliance and leadership in product development.

The Engineering Manager for Smart Signals leads a team, manages execution, and maintains technical depth in backend systems for fraud detection. They shape strategy and coordinate across teams to enhance product quality.

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.

Design and build backend systems and APIs for AI-driven products, ensuring scalability, reliability, and integration of ML models while collaborating with engineering and product teams.

The Senior Manager will oversee the fraud lifecycle, optimize detection strategies, conduct fraud reviews, collaborate with teams, and manage risk reporting.

Design, optimize, and scale ClickUp's search infrastructure using OpenSearch/ElasticSearch. Improve relevance, speed, and real-time indexing; build vector/semantic search capabilities; create measurement frameworks; integrate search with AI and product features; troubleshoot at scale; and mentor junior engineers to maintain high search quality under heavy load.