Top Machine Learning Jobs in Richmond, VA

Leads strategic analysis and product innovation for Capital One’s Consumer and Developer Experience organization. Responsibilities include answering complex strategic questions, leading AI transformation initiatives, conducting analytics and market assessments, setting long-term product vision, developing prototypes with engineering, building stakeholder relationships, and advancing organizational capabilities through training and AI adoption.

Leads data science and model risk initiatives, partnering with data scientists, engineers, and product managers. Builds, evaluates, validates, and implements machine learning models using large-scale data and technologies including Python, AWS, H2O, and Spark. Translates complex statistical findings into business outcomes, researches emerging methods, validates and backtests models, and develops talent. Requires quantitative education, substantial analytics experience, machine learning expertise, and relational database experience.

Leads product management for Capital One’s Enterprise Data team, aligning product, technology, design, machine learning, and risk partners. Builds a modern data ecosystem and unified data destination using platforms such as OneStream and OneLake. Drives data trust, governance, discoverability, data product scaling, external data adoption, and customer-focused digital experiences. The role requires translating strategy into products, solving complex problems, mitigating risks, and leading cross-functional teams through transformational change.

Lead product management initiatives for Capital One’s Collections and Recoveries organization. Partner with technology, analytics, operations, risk, legal, design, marketing, and compliance teams to discover customer needs, define roadmaps, launch scalable digital capabilities, and improve recoveries. Potential focus areas include operational or analytical data infrastructure, recoveries platforms, payments and offers, or web and mobile customer experiences.

Leads product strategy and execution for HR data and AI platforms supporting performance assessment, associate growth, and organizational planning. Builds scalable data foundations, signal engines, AI-ready ecosystems, enterprise integrations, and onboarding experiences. Partners with engineering and cross-functional teams to deliver reliable data products, enable machine learning adoption, and shape HR data strategy across the organization.

Design, build, deploy, and support scalable cloud data solutions, pipelines, platforms, and applications for enterprise risk management. The role uses Python, Spark, SQL, relational and NoSQL databases, Databricks, Snowflake, and cloud technologies. Responsibilities include data modeling, distributed and streaming workloads, security controls, architecture standards, observability, operational efficiency, stakeholder communication, mentoring, and collaboration with product, engineering, analytics, and machine learning teams.

Manage data analytics for PULSE Risk & Compliance by analyzing large datasets, identifying transaction anomalies and risk exposure, validating models, improving reporting and processes, and supporting compliant data strategies. The role involves Python-based programming, BI, database and data-storage design, strategic testing, cross-functional collaboration, project leadership, and people management in a hybrid environment.

Build and deploy production-grade AI and machine learning solutions for marketing and sales, including LLM agents, data-quality pipelines, semantic search, RAG, analytics, and integrations with Salesforce, Marketo, and Adobe platforms. Define evaluation and governance practices, monitor AI services, implement privacy controls, and collaborate with platform teams. Train and mentor technical and business stakeholders on responsible AI adoption while supporting DevOps, incident response, and operational runbooks.

Develop and optimize full-funnel, multi-channel demand generation campaigns that generate pipeline, accelerate deals, and support cross-sell and upsell opportunities. Partner with product marketing, sales development, content, product, field, partner marketing, and analytics teams. Build lead-nurturing programs, align assets to buyer stages, monitor conversion and ROI metrics, and create dashboards to optimize campaign performance.

Develop machine learning and statistical models across the full lifecycle, including design, training, evaluation, validation, and implementation. Analyze large-scale numeric and textual datasets using Python, cloud computing, and distributed data technologies. Collaborate with data scientists, software engineers, and product managers to translate complex analyses into customer-focused business outcomes. Apply methods including classification, clustering, sentiment analysis, time series, and deep learning.

Leads a multi-team organization building AI-driven cybersecurity systems for endpoint and cloud environments. Owns technical strategy, roadmap, hiring, delivery, evaluation, and safe production deployment of AI models and autonomous defense agents. Oversees systems operating under strict endpoint resource limits and at cloud scale across trillions of events. Partners with engineering, product, threat intelligence, and executives while advancing security efficacy, reliability, and responsible AI adoption.

Leads alternative data strategy for credit decisioning by evaluating vendors and datasets, engineering features, building and validating machine learning models, and developing scalable processing and monitoring systems. Partners with data scientists, software engineers, and product managers while translating complex analyses into business outcomes. The role requires expertise in large-scale data analysis, statistical modeling, machine learning, and relational databases, with hybrid office attendance.

Leads AI transformation, governance, quality oversight, risk management, and responsible adoption across Pfizer Global Supply and QSEO. Establishes GxP-compliant AI lifecycle practices, inspection-readiness frameworks, governance forums, portfolio monitoring, inventory management, metrics, and reporting. Advises senior leaders on AI regulations, deployment risks, investment priorities, and compliance while leading cross-functional initiatives and influencing stakeholders across Quality, Digital, Operations, and R&D.

Leads the development and deployment of data solutions using Palantir Foundry and AIP. Manages engineering teams, coaches team members, oversees project planning and execution, analyzes system interactions, manages client relationships, and drives innovation. The role applies software engineering, data engineering, analytics, and AI/ML techniques to deliver scalable client solutions while maintaining technical and professional standards.

Leads the development and deployment of software and data solutions using Palantir Foundry and AI/ML technologies. Responsibilities include data engineering, analytics, system improvement, client account management, project ownership, team leadership, coaching, stakeholder engagement, and continuous improvement. The role requires building scalable applications with large datasets and delivering customer-focused solutions.

Own and grow strategic enterprise customer relationships, driving retention, renewals, expansion, and seven-figure software transactions. Develop multi-year account plans, build executive and technical stakeholder relationships, identify expansion opportunities, lead negotiations, maintain forecasts and pipeline visibility, and coordinate cross-functional teams. Engage with data scientists and AI/ML engineers to understand adoption, customer outcomes, and opportunities to expand platform usage across business units and use cases.

Own the strategic vision and roadmap for Coursera’s AI-driven search experience. Lead conversational search, personalization, semantic and multilingual search, ranking, and discovery improvements. Partner with engineering, design, research, data science, ML, marketing, and partnerships teams to deliver scalable platform capabilities. Use experimentation, market analysis, KPIs, and customer insights to improve conversion, merchandising, learner experience, and revenue.

Leads GitLab’s Product Data Science team, setting its strategy, roadmap, analytical standards, and priorities. Partners with Product and Engineering leaders on experimentation, causal inference, analytics, and production modeling. Manages, hires, coaches, and retains data scientists while building a high-performing function. Provides hands-on technical review of models, analyses, and queries, and communicates insights and risks to senior leaders.

The Data Scientist will partner with Sales, Revenue Operations, and Finance to analyze GTM performance, build and productionize machine learning models, conduct experiments, forecast outcomes, and identify growth opportunities. Responsibilities include statistical analysis, causal inference, Python and SQL development, MLOps, agentic workflow automation, semantic-layer contributions, executive reporting, and communicating insights to nontechnical stakeholders in a fully remote environment.

Deploys, configures, optimizes, and supports CrowdStrike Falcon’s AI-powered detection and response capabilities. The role tunes machine learning models, develops security automation playbooks, troubleshoots customer environments, integrates Falcon with existing tools, supports threat hunting and forensic analysis, and develops AI training and customer-facing materials. Requires extensive IT deployment, endpoint, security automation, operating system, AI/ML, scripting, and customer consulting experience, with travel up to 25%.

Leads SailPoint’s global corporate communications function, including corporate narrative, media relations, PR campaigns, crisis communications, executive coaching, podcast strategy, partner communications, and measurement. The role manages internal and agency teams, translates complex cybersecurity and AI concepts into compelling stories, supports product launches and company milestones, builds rapid-response capabilities, and advises executives on reputation and communications strategy.

Own the strategy, development, and lifecycle of scalable data products supporting analytics and machine learning. Partner with technology, business, and risk teams to translate needs into requirements, guide agile delivery, improve data quality and reliability, and enable self-service insights through usable, high-performing datasets and visualizations.

Own Secret Detection and Vulnerability Research as a product: define strategy across prevention, detection, validation, revocation, and reporting; ship detection content and intelligence with measurable quality; drive adoption, retention, and revenue; partner with engineering, research, and Field; use AI to accelerate research and productization.

Analyzes the Security for AI competitive landscape, including AI detection and response, generative AI security, autonomous agents, prompt injection, and data leakage. Partners with Product, Sales, Marketing, Strategy, and senior leaders to guide product roadmaps, go-to-market strategy, deal support, sales tactics, and competitive positioning. Conducts win/loss analysis and translates complex AI and cybersecurity concepts into executive and field-actionable guidance.

Leads data science efforts for foundational AI and automation across a global payments network. Partners with data scientists, engineers, and product managers to develop scalable machine learning engines, AI agents, merchant intelligence, and risk solutions. Serves as an NLP and LLM domain expert, fine-tuning and deploying models using PyTorch, AWS, vector databases, and modern AI technologies. Translates complex ML work into business value while mentoring teams and delivering production-scale platforms and solutions.