Top Senior Machine Learning Jobs in Richmond, VA

Drives business transformation and strategic change initiatives by developing transformation strategies, improving processes and organizational design, managing change adoption, engaging senior stakeholders, coordinating cross-functional projects, delivering training, and tracking benefits realization. The role manages transformation projects from planning through post-implementation review while ensuring alignment with business objectives, timely delivery, budget adherence, and successful organizational adoption.

Develop fraud detection and identity-risk machine learning models from problem definition through production deployment. Responsibilities include investigating fraud patterns, exploratory analysis, feature engineering, model training and evaluation, production-quality Python and PySpark development, monitoring model and feature drift, and presenting analytical recommendations. The role requires collaboration with data scientists and engineers while addressing privacy, explainability, validation, governance, and real-world fraud-modeling challenges such as class imbalance and delayed labels.

Leads AML transaction monitoring optimization, including rule testing, threshold tuning, scenario design, and alert reduction. Drives FIU automation and workflow improvements, evaluates AI-enabled compliance tools, and partners with Product, Engineering, Data, Operations, and vendors on monitoring infrastructure and integrations. Supports scalable compliance controls across new products, jurisdictions, and transaction volumes while maintaining regulatory alignment and detection effectiveness.

Manage enterprise metadata, business glossaries, data catalogs, and end-to-end data lineage for a federal case management modernization program. Configure governance and metadata platforms, integrate data sources and pipelines, establish standards and workflows, monitor metadata quality, support compliant data sharing, develop governance metrics, document procedures, and train data stewards and business users.

Build and deploy production LLM applications and AI agents, including RAG pipelines, document processing, APIs, tool calling, and multi-step workflows. Develop backend services in Python, integrate major model providers, and deploy cloud-based containerized solutions through CI/CD. Implement evaluation, monitoring, security controls, audit logging, and federal authorization documentation. Collaborate with platform, DevOps, security, and government stakeholders while documenting architectures, advising users, and mentoring engineers.

Develops data quality checks, metadata standards, and AI use-case inventories for HHS. Cleans and integrates complex datasets, applies machine learning and NLP for automated tagging and classification, evaluates AI governance risks, and produces reporting datasets, dashboards, and recommendations. The role requires translating ambiguous federal policy and reporting requirements into repeatable analytical workflows while collaborating with product, design, engineering, and government stakeholders.

Develop and deploy machine learning and LLM-based solutions for cybersecurity challenges. Responsibilities include analyzing complex datasets, building classification and NLP models, optimizing prompting, embeddings, RAG, and fine-tuning techniques, designing evaluation methodologies, and integrating models into scalable production systems. The role requires collaboration with cybersecurity researchers, software engineers, data engineers, and product managers.

Own the authorization optimization product roadmap, improving legitimate transaction approval rates and recovered merchant revenue. Define technical requirements, APIs, data models, integrations, and issuer-specific decisioning strategies. Partner with engineering, data science, sales, solutions, and customer success to deliver fraud-aware authorization improvements involving retries, 3DS, decline recovery, and payment routing. Manage partnerships with payment ecosystem providers, quantify opportunities using issuer and BIN intelligence, and communicate product strategy and performance to stakeholders.

Leads the design, implementation, and support of complex, scalable software components and distributed systems. Provides technical leadership through architecture decisions, mentoring, code and design reviews, troubleshooting, and engineering standards. Partners with product, quality, architecture, and DevOps teams on secure cloud-native solutions, CI/CD, observability, performance, releases, and platform modernization. Participates in hiring and communicates technical risks and tradeoffs to stakeholders.

Owns the AI strategy, roadmap, and initiative portfolio for assigned business areas. Identifies and prioritizes AI opportunities, translates business needs into compliant AI solutions, and partners with stakeholders, IT, and AI teams to deliver outcomes. Builds less complex AI agents using approved tools, supports technology adoption, monitors KPIs, and uses analytics, feedback, and market trends to guide continuous improvement and investment decisions.

Leads biostatistical activities for Phase II–III clinical trials, from protocol design through reporting and regulatory submission support. Responsibilities include statistical analysis planning, sample size calculations, SAP development, TLF review, database lock support, safety reporting, regulatory responses, and oversight of programming or CRO work. Collaborates with cross-functional clinical teams and uses SAS or R for analyses, modeling, simulation, exploratory work, and quality control. Coordinates statistical activities across studies and applies advanced statistical and machine learning methods.

Leads statistical activities for Phase II–III clinical trials from protocol design through reporting and regulatory submissions. Develops statistical analysis plans, sample-size calculations, and reviews tables, listings, and figures. Oversees internal programmers and CRO contributions, applies SAS and R for analyses and quality control, and collaborates with clinical, data management, programming, and regulatory stakeholders. Coordinates statistical activities across studies and applies advanced statistical, machine learning, and modeling methods to support drug development.

Develop and standardize analytical methods for inferring residence, displacement, and poverty from mobile network operator data. Assess data quality, write technical specifications and tests, guide operator engineering teams, tune statistical methods, and balance accuracy with implementation constraints. Design privacy-preserving analyses, improve data security practices, train internal teams, and support international workshops and emergency-response programs.

Resolve complex customer technical issues across Linux, networking, security, Kubernetes, cloud, storage, and ML environments. Diagnose problems, automate workflows with Python and Bash, support model deployment and inference pipelines, communicate resolutions, escalate unresolved issues, and maintain troubleshooting documentation. The role requires rotational shifts or on-call availability and remote work in the USA.

Senior cheminformatics professional responsible for building and maintaining scalable chemical databases, search systems, indexing, similarity analysis, and analytics workflows. The role develops agentic AI solutions for drug and pipeline analysis, integrates scientific literature with computational tools, and applies machine learning to pharmaceutical research and market analytics. Candidates need advanced chemistry or related scientific education, cheminformatics expertise, pharmaceutical or biotech experience, and strong interdisciplinary communication skills.

Develop and own advanced machine learning and AI models using bank transaction and cashflow data to enable financial product capabilities, manage risk, and drive profitable growth. Design experiments, build proofs of concept, monitor production models, and collaborate with data and machine learning engineering teams on scalability. Communicate findings and tradeoffs to business stakeholders while applying statistical, research, and financial services expertise.

Own the strategy, roadmap, architecture, performance, and business outcomes of Versapay’s global payment gateway. Lead processor and acquirer integrations, transaction lifecycle capabilities, compliance, reliability, and payment optimization. Partner with engineering, finance, risk, compliance, operations, and go-to-market teams while guiding execution across engineering pods. Apply AI and machine learning to retry optimization, decline analysis, anomaly detection, incident response, and partner integrations.

Develop AI agents, chatbots, and machine learning models for a modernized federal data science platform. Research and standardize AI capabilities in Databricks and Palantir, prompt-engineer LLMs, integrate AI into DevOps pipelines, and train models on production datasets for prediction and anomaly detection. The role requires collaboration with developers and customers, database familiarity, Agile experience, documentation, and an active or obtainable DoD Secret clearance.

Leads pricing and revenue data science initiatives for consumer markets clients. Develops predictive models, forecasts trends and customer behavior, manages dashboards and visualizations, ensures data quality, and translates complex analytics into strategic decisions. Oversees client engagements, validates outcomes, directs complex projects, and coaches diverse, high-performing teams.

Lead computational platform development for biological design, optimization, and functional prediction. Collaborate with wet-lab scientists to validate predictions, oversee data infrastructure and computational workflows, and contribute to technology roadmaps, funding proposals, and partnerships. The role requires developing machine learning methods for biological sequence and structure analysis, translating biological problems into computational solutions, and leading technical teams across computational and experimental disciplines.

Build and deploy machine learning models that personalize insurance products, analyze product data, design experiments, and measure model impact. The role owns the full data science lifecycle, including data wrangling, production coding, model monitoring, retraining, and experimentation. Responsibilities also include cross-functional collaboration, code reviews, mentoring data scientists, and improving platform practices.

Leads client-facing manufacturing and automotive analytics, data, AI, and digital transformation engagements. Responsibilities include executive stakeholder management, consulting, solution design, account growth, proposal development, commercial oversight, program governance, delivery leadership, and capability building. The role develops transformation roadmaps, modernizes data platforms, guides AI and analytics use cases, manages strategic portfolios up to $10M–$15M annually, and mentors multidisciplinary teams.

Lead the development, optimization, and production deployment of advanced machine learning algorithms for advertising effectiveness and ROI. Build scalable ML models, write efficient code, collaborate with machine learning engineers, prototype and test algorithms and data pipelines, and drive iterative improvements using historical data and rigorous performance analysis.

Lead Client Legal Services analytics strategy and team development. Own Power BI reporting products, Microsoft Fabric data solutions, governance, predictive modeling, responsible AI adoption, ROI measurement, and executive insights. Partner cross-functionally to improve legal operations, litigation outcomes, staffing, capacity, cost management, quality, and workflow optimization while building scalable analytics capabilities and institutional knowledge.

Lead research into novel AI architectures, learning algorithms, reasoning, knowledge representation, and evaluation methodologies. Define and execute ambitious research programs, design experiments, develop large-scale content evaluation systems, mentor research engineers, and communicate findings through technical publications. The role combines scientific leadership with hands-on innovation focused on language models, factuality, evidence quality, source reliability, and trustworthy AI systems.