Top Senior Data Science Jobs

Lead Data Scientist partners with business leaders to identify opportunities, design, develop, and deploy advanced analytics and ML solutions. Responsibilities include data preparation, modeling, feature engineering, delivering insights and visualizations, establishing standards, and directing associate data scientists, contractors, and consultants to produce measurable business value.

Lead data science and analytics initiatives supporting product, engineering, leadership, and insurance clients. Analyze claims and product data, design experiments, generate hypotheses, measure ROI, build dashboards and reproducible analytics, and communicate actionable recommendations. Mentor junior analysts, conduct client-facing analyses, collaborate with ML teams, investigate data issues, and shape product strategy through sophisticated statistical modeling and exploratory analysis.

Develops algorithms, predictive analytics, automated workflows, and supervised and unsupervised machine learning models for federal health organizations. Analyzes complex structured and unstructured data, communicates findings to customers and nontechnical stakeholders, briefs military leaders, mentors teammates, and supports operational requirements. The role requires Python development, a TS/SCI clearance, travel within the continental U.S. up to 25%, and possible international deployments of up to 90 days.

Develop and enhance Palantir Foundry applications for tracking and forecasting space-launch equipment costs. Build data models, visualizations, automated workflows, and Python scripts to analyze complex datasets. Translate findings into actionable recommendations, collaborate with project teams, and produce user guides and technical documentation.

Conducts data analytics, engineering, mining, statistical analysis, predictive modeling, and intelligence-focused data visualization. Designs and optimizes data architectures, storage, access, integration, throughput, and query performance. Builds AI automation tools and prediction systems from disparate data sources, collaborates with intelligence community members, communicates complex findings, and develops concise defense intelligence products. Requires active TS/SCI clearance, U.S. citizenship, and ability to obtain a counterintelligence polygraph.

Leads advanced analytics initiatives supporting strategic planning, equity tracking, forecasting, budgeting, staffing, and resource allocation. Builds predictive models, simulations, and decision-support tools using education and enterprise data. Presents insights to executive and board-level audiences, partners with academic, operational, and finance teams, maintains reproducible workflows and data governance compliance, collaborates on data quality, and mentors analysts.

Own enterprise data inventory and translate business needs into data models, quality rules, mappings, and acceptance criteria. Partner with data and software teams on pipelines, governance, monitoring, and lifecycle management. Prepare data for agentic workflows, including semantic layers, knowledge bases, retrieval indexes, and evaluation datasets. Develop reusable analytical models, reports, dashboards, and decision-support products while monitoring data quality and communicating limitations and business impact.

Analyze big data using data mining, machine learning, and sentiment analysis techniques. Develop and apply data architecture solutions using Java open-source technologies and OpenNLP. The role requires expertise in big data analytics and at least six months of engagement.

Analyze large datasets using data architecture, Java, sentiment analysis, OpenNLP, data mining, and machine learning techniques. The role requires developing or supporting big data analytics solutions and applying machine learning methods to derive insights from complex data.

Lead technical direction for homeowners insurance pricing and risk modeling. Architect and deploy GLM and machine learning models, including profitability, retention, conversion, and customer lifetime value models. Use geospatial data to assess localized risk, partner with actuarial, product, and engineering teams on regulatory filings and production workflows, mentor data scientists, and champion experimentation, monitoring, MLOps, and emerging generative AI applications.

Develop and deploy machine learning models and statistical algorithms to detect financial crime using large-scale BSA/AML transaction data. Perform exploratory analysis, feature engineering, validation, SQL analysis, visualization, and documentation using Python, R, PySpark, AWS, and related tools. Collaborate with investigators, compliance analysts, and technical stakeholders to translate regulatory requirements into analytical solutions. The role requires active Top Secret clearance with SCI eligibility and full-time onsite work in Washington, DC.

Works with business stakeholders to identify data-driven opportunities, analyze trends, and support business decisions. Designs data models, machine-learning algorithms, and predictive analytics models; preprocesses structured and unstructured data; identifies data sources and automates collection; analyzes large datasets for patterns; and presents insights through data visualization.

Leads data science work involving data exploration, cleaning, analysis, visualization, mining, predictive modeling, and algorithm development. Analyzes structured and unstructured data using statistical and programming tools, applies machine learning and AI techniques, and translates complex datasets into actionable insights for clients. Guides teammates, develops data-driven systems, and advises decision-makers across public and private sector challenges.

Lead the research, design, implementation, and deployment of AI models, agentic solutions, and optimization algorithms for enterprise applications. Partner with customers and cross-functional teams to build scalable AI applications, improve model performance, and integrate validated data assets. Contribute to new platform capabilities while applying generative AI, large language models, classical machine learning, and statistical methods in reliable, production-scale environments.

Designs, prototypes, evaluates, and improves LLM-powered legal AI systems and agentic workflows. Responsibilities include prompt engineering, RAG, retrieval and ranking, model experimentation, evaluation benchmarks, error analysis, monitoring, and production-oriented coding. The role engages lawyers and enterprise stakeholders to define measurable problems, validate AI experiences, and establish quality standards for accuracy, grounding, hallucination detection, tool use, reliability, cost, and latency.

Evaluates and improves statistical models, machine learning algorithms, and analytic workflows supporting classified intelligence and Space and Counterspace missions. Analyzes complex datasets, validates algorithm performance, assesses data quality, integrates new analytic approaches, and provides technical recommendations and briefings to government stakeholders. Collaborates with engineers, developers, analysts, and data science teams while ensuring security and data governance compliance.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

Analyze and characterize conversational audio data, identify underrepresented areas, and build active-learning and human-in-the-loop workflows. Develop representative benchmarks, automate model adaptation, and create reusable data analysis and scoring systems. Partner with research, engineering, and data teams to run experiments, improve models, organize data, and translate complex findings into measurable product improvements.

Designs and delivers machine-learning, analytics, NLP, and generative-AI solutions for healthcare and regulated federal data environments. Builds production workflows, predictive models, classification systems, and decision-support tools using structured and unstructured data. Owns data quality, experimentation, evaluation, validation, monitoring, governance, privacy, and deployment decisions. Collaborates with technical teams, domain experts, clients, and leadership to solve ambiguous, technically complex problems.

Develop and maintain advanced inventory and supply planning models, inventory optimization targets, scenario analyses, dashboards, and KPIs. Use Python, SQL, BI tools, predictive modeling, and machine learning to support supply planning, S&OE/S&OP, demand-supply coverage, capacity constraints, and inventory health. Partner with planning teams to improve data quality, automation, governance, model reliability, and executive decision-making.

Lead growth data science for emerging healthcare products by defining measurement frameworks, analyzing funnels, designing experiments, building predictive models and segmentations, and creating reliable data foundations, dashboards, and self-service tools. Partner with Marketing, Product, Engineering, Design, and Data Science to optimize acquisition, engagement, conversion, launches, and product growth.

Leads product analytics through advanced analysis, experimentation, KPI development, dashboards, and data storytelling. Designs A/B tests and causal analyses to optimize product performance and user experience. Owns measurement for Xometry’s embedded DFM AI and IQE integration across CAD/PLM partner surfaces and the marketplace, addressing attribution, identity resolution, and low-volume enterprise cohort challenges. Collaborates with product, engineering, design, machine learning, and partner teams to deliver actionable insights and improve experimentation methodologies.

Lead product analytics for Xometry’s marketplace and partner-integrated CAD/PLM experiences. Design and analyze A/B tests, causal studies, KPI frameworks, dashboards, and cross-surface measurement strategies. Evaluate user behavior, product performance, and digital-thread outcomes across partner and marketplace funnels. Translate statistical findings into product recommendations, collaborate with product and engineering teams, ensure data integrity, and communicate insights to leadership and external partners.

Analyze product and user data to guide product strategy, operations, and roadmaps. Design experiments, apply causal inference, investigate anomalies, define metrics, build statistical models and simulations, and establish scalable analytics practices. Present actionable insights to technical and business stakeholders while supporting data-informed decision-making for AI products.

Develops scalable data pipelines, parsers, and database systems for enterprise AI applications. Collaborates with AI/ML teams to optimize data access, retrieval, quality, security, and infrastructure performance. Supports AI application deployment and maintenance while evaluating machine learning models and statistical techniques. The role requires expertise in Python, databases, Docker, Kubernetes, data engineering, defensive cyber operations, and machine learning, with potential supervision of others.