Top Data Science Jobs

Develops and deploys AI, generative AI, LLM, and agentic workflow solutions for pharmaceutical therapeutics development and supply. Responsibilities include building retrieval and evidence synthesis systems, establishing LLM evaluation frameworks, applying responsible AI and governance practices, and maintaining software engineering standards. The role collaborates with CMC, regulatory, quality, scientific, manufacturing, supply chain, and AI/ML stakeholders to deliver scalable solutions in regulated environments.

Leads end-to-end research and development of statistical and machine learning models for media measurement. Analyzes large datasets, validates methodologies, collaborates with engineering and product teams on scalable model deployment, and communicates insights through visualizations and presentations. Requires advanced statistical expertise, machine learning proficiency, Python, and substantial independent data science experience.

Lead hands-on delivery of agentic conversational AI solutions for banking IVR systems using Amazon Connect and Amazon Lex. Conduct experimentation, interaction-data analysis, model evaluation, intent and slot tuning, prompt development, and NLU performance optimization. Support LLM and GenAI capabilities, identify risks, and communicate findings and recommendations to leadership and project stakeholders.

Develop AI and data science algorithms for spacecraft autonomy, mission operations, scientific analysis, planning, scheduling, resource allocation, and execution automation. Apply search-based, constraint-based, satisfaction, optimization, and geometric reasoning techniques to autonomous systems supporting space, marine, and aerial vehicle missions.

Lead design and analysis of large experiments and observational studies; select causal inference methods; develop and productionize ML and causal models; set experimentation best practices; influence metrics and measurement; mentor junior data scientists; present results to senior stakeholders.

Develop machine learning, statistical, graph-based, and data-mining algorithms for national-security missions. Build, optimize, and validate AI/ML and large language models; create feature vectors, experiments, simulations, visualizations, and predictive analytics; and implement prototypes in production frameworks. Collaborate with subject-matter experts to automate analysis, guide analytic development, and deliver data-driven insights. Experience with LLMs, RAG, agents, GPU computing, and active TS/SCI clearance with polygraph is required.

Lead games data science for Disney's Fortnite partnership: develop advanced analytics, forecasting, experimentation, and behavioral segmentation; analyze telemetry and engagement data; define KPIs and measurement frameworks; build scalable analytics infrastructure and dashboards; translate findings into strategic recommendations for product, marketing, and executive stakeholders.

Develops and deploys machine learning models and data solutions using Python, R, and SQL. Responsibilities include data cleansing, statistical analysis, visualization, dashboard and web application development, system integration, and applying supervised and unsupervised learning to structured and unstructured data. The role involves advising clients, guiding teammates, developing algorithms, and working with data-rich environments. A TS/SCI clearance with polygraph and bachelor's degree are required.

Provide data analysis, graphics, and dashboards to identify challenges and opportunities. Use Python or R, SQL, relational and NoSQL databases, Tableau, and time-series analysis to transform unstructured data into actionable insights supporting customer decisions about production, resources, and personnel.

Lead data science measurement initiatives for media and advertising. Design experiments and causal models to measure incrementality, build marketing mix models, and productionize scalable data pipelines. Develop and deploy models using Python, R, SQL, and cloud platforms while ensuring data quality and privacy compliance. Deliver dashboards, automated reporting, and executive-ready insights, communicate findings to stakeholders, and mentor junior data scientists.

Design and deliver end-to-end data science solutions for complex client and business problems. Apply machine learning, statistical modeling, and AI to develop scalable analytical products, translate business needs into analytical frameworks, and guide deployment. Partner with clients and stakeholders, communicate insights, uphold technical and governance standards, mentor junior team members, and support business development through solution scoping, proposals, and presentations.

Develop proprietary foundation models, fine-tune domain-specific models, and architect multi-agent systems with autonomous reasoning, tool calling, and advanced RAG. Build curated and synthetic datasets for pretraining, alignment, and benchmarking. The role requires deep generative AI expertise, including LLM development, agent frameworks, foundation model architecture, and production-grade RAG pipelines, along with proficiency in modern model training, inference, vector infrastructure, and data technologies.

Lead data science projects across R&D, manufacturing, sales, and supply chain. Prepare and clean data, build regression and machine learning models (random forest, xgboost, lasso), perform cross-validation and hyperparameter tuning, query data with SQL, create plots and reports, communicate results to nontechnical stakeholders, identify high-value problems, and collaborate with data engineering and software teams to deploy models.

Leads data science initiatives by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Develops predictive models and algorithms, translates complex datasets into actionable insights, advises clients, and guides teammates. Requires Python, SQL, statistical programming, TS/SCI clearance, DoD 8140 Level II certification, and up to 25% travel. Intelligence and geospatial analytics experience are preferred.

Develops and deploys AI and machine learning models, algorithms, and analytics for federal customers. Responsibilities include analyzing datasets, testing and refining models, developing statistical and graph-based algorithms, creating visualizations, automating manual analysis, and integrating prototype solutions into production analyst workflows. Requires quantitative education, extensive data analysis and programming experience, and a TS/SCI clearance with polygraph.

Develop AI-enabled supply risk analytics using machine learning, probabilistic modeling, Monte Carlo simulation, forecasting, anomaly detection, and Generative AI. Build interactive applications, data pipelines, agentic workflows, and reporting automations using Python, SQL, Databricks, AWS Glue, and related tools. Collaborate with global Supply Chain, Quality, Procurement, and Business Continuity stakeholders to translate risk questions into actionable insights while following responsible AI, data privacy, quality, testing, and agile development practices.

Data Scientist Intern will collaborate with business teams in an agile environment to develop insurance risk, claims, retention, anomaly detection, and process-efficiency models. The role includes using internal and third-party data, generalized linear models, machine learning, AI techniques, and data visualization. Candidates should be pursuing a relevant degree, maintain a minimum 3.0 GPA, and demonstrate quantitative, programming, communication, analytical, and teamwork skills. This is an in-office internship in West Bend or Madison, Wisconsin.

Analyze complex operational, program, schedule, requirements, risk, and workstream data to create trusted metrics, dashboards, models, and decision-support products. Define KPIs and analytical logic, assess data quality, develop Python/R/SQL analyses, and support AI-enabled workflows. Partner with architects, engineers, leadership, and mission stakeholders to translate ambiguous operational questions into measurable insights and recommendations for FAA modernization and Digital Command Center initiatives.

Supports equity research analysts by querying, transforming, analyzing, and combining large datasets; developing statistical models; building AI agents, MCP servers, and data workflows; and ensuring data quality, lineage, governance, and AI accessibility. The role translates complex technical methods into actionable insights, collaborates with research teams and vendors, and contributes to generative AI tooling and platform enhancements.

Supports equity research analysts by querying, transforming, analyzing, and combining large datasets; developing statistical models; and building AI tools, agents, MCP servers, and data processes. Ensures datasets meet accessibility, quality, lineage, and governance standards. Translates technical methods into actionable insights for nontechnical stakeholders while collaborating with research teams, vendors, management, and clients.

Performs data science for a Joint Interagency Task Force supporting counter-UAS capabilities. Develops data taxonomies, analyzes data trends, provides data modeling for algorithms, and creates data-sharing mechanisms with external stakeholders. The role requires systems and cloud administration expertise, preferably with Microsoft Azure Government and Palantir Foundry, and involves onsite work in Alexandria, Virginia with occasional field travel.

Develops predictive models and transforms structured and unstructured data into actionable insights. Responsibilities include data wrangling, statistical analysis, machine learning, visualization, reporting, data pipeline management, cloud and DevSecOps integration, root-cause analysis, and technical solution development. The role supports government stakeholders, guides junior personnel, leads data projects independently, validates AI models, documents algorithms, and communicates findings through briefings and presentations.

Analyze data to identify trends and anomalies, develop machine learning and statistical models, perform data parsing and ETL with Python and SQL, create dashboards, and collaborate with data engineers. The role emphasizes NLP, LLM, and Generative AI applications, quantitative analysis, model validation, and experimentation with emerging tools for federal clients.

Develop audience segments, customer profiles, and predictive models using behavioral, transactional, viewing, and digital data. Apply statistical and machine learning methods for clustering, churn, propensity, content affinity, and customer value analysis. Conduct exploratory analysis, evaluate models, support campaign measurement, and translate findings into business recommendations. Collaborate with Marketing, Product, Media Sales, and Content teams while presenting insights to technical and non-technical stakeholders.

Administers UMM data-management infrastructure, including access controls, configuration, dashboards, data flows, ingestion validation, monitoring, and security compliance. Provides advanced tier-two technical support, troubleshoots issues, supports testing and user acceptance, documents configurations, and coordinates enhancements with engineering and data stakeholders. The role supports distributed DoD and Intelligence Community users and requires on-site work in a DIA SCIF with an active TS/SCI CI Poly clearance.