Top Data Science Jobs

Own the Ad Network data stack across modeling, BI reporting, analytics, experimentation, entity resolution, and machine learning. Build ClickHouse data models, dashboards, aggregation tables, attribution logic, and KPI tracking. Conduct statistical analyses and A/B tests, monitor data quality, uncover network insights, and translate findings into product recommendations. Collaborate with product, business, engineering, and ML teams to improve advertiser and publisher outcomes.

Own the analytical strategy for a digital primary care program by defining KPIs, building dbt data models, maintaining the Snowflake/Fivetran/dbt stack, and delivering analytics, forecasting, revenue modeling, dashboards, and AI-assisted workflows. Translate patient, EHR, clinical, and operational data into actionable insights for product, care delivery, operations, leadership, health systems, and payers.

Leads data science research for a breast cancer program, developing multimodal clinical data frameworks, data dictionaries, machine learning pipelines, statistical models, and reproducible genomic analyses. Collaborates on computational tools, manages projects, drafts grants, creates training materials, supervises trainees, and supports complex data requests involving clinical, genomic, pathology, imaging, and administrative datasets.

Develops predictive and prescriptive models, machine learning solutions, dashboards, and LLM-based chatbots for clinical, operational, financial, and research applications. Collaborates with healthcare, academic, medical, engineering, and business stakeholders to translate data insights into decisions. Responsibilities include the machine learning lifecycle, data products, web applications, documentation, model interpretation, data quality, and cancer epidemiology research communications.

Serve as Netflix Animation Studios’ founding data scientist, developing measurement frameworks, forecasting and scenario-planning models, dashboards, and data products for slate planning, production efficiency, workforce allocation, and strategic decision-making. Partner with studio leadership, data scientists, analytics engineers, and data engineers to establish data foundations, define metrics, analyze operational drivers, and translate complex findings into actionable recommendations.

Develop data collection tools, visualizations, dashboards, algorithms, and scripts to analyze structured and unstructured data. Build predictive analytics and machine learning solutions, integrate models into production applications, and create API-based data pipelines. Translate complex data into actionable insights for Air Force mission leaders while managing enterprise-scale data science projects and communicating findings to technical and non-technical audiences.

Develop algorithms, predictive models, quantitative analyses, and visualizations from structured and unstructured data. Apply Python and statistical programming to data exploration, cleaning, mining, and analysis while using machine learning, AI, and NLP techniques. Collaborate with clients to translate data into actionable insights and support informed decisions across public and private-sector challenges.

The Data Scientist will conduct statistical analyses, develop reproducible code, manage data projects and pipelines, improve data governance, monitor data quality, maintain documentation and data dictionaries, and support transportation policy initiatives. The role also involves collaborating with technical and non-technical stakeholders, presenting findings, responding to analytical requests, improving databases and aggregate tables, and mentoring team members.

Analyze bike-share and shared-mobility data, develop predictive models, create Power BI dashboards, maintain databases and data quality, monitor vendor compliance, and provide research-based recommendations for planning, operations, policy, and future technology requirements.

Performs exploratory data analysis, feature engineering, predictive and prescriptive modeling, statistical testing, visualization, and business problem solving. Develops, validates, and deploys models in partnership with engineering and product teams, improves data sources and internal data platforms, communicates findings to stakeholders, and mentors associate data scientists.

Build and validate predictive lending models, analyze consumer risk and portfolio impact, implement and monitor models with engineering, develop tooling and automated SQL/Python dashboards, design A/B tests, and communicate actionable insights to senior management.

Apply statistical inference and experimentation to evaluate product changes, identify Growth opportunities, and guide strategy. Partner with product, engineering, and marketing leaders to define metrics, build datasets and dashboards, and communicate insights that drive business and product decisions. The role requires SQL, scripting, data transformation, and practical tradeoffs between speed and accuracy. Predictive modeling experience is preferred, as are startup and B2B SaaS backgrounds.

Serve as the analytics and data science partner for Workday Data Cloud, connecting demand, sales pipeline, product usage, and consumption-based pricing. Investigate ambiguous business questions, derive decision-ready insights, create visualizations and executive narratives, and advise senior stakeholders. Build curated data assets, improve data connections and pipelines, address instrumentation gaps, and collaborate with Product, Engineering, and GTM teams to support strategic growth and data-driven decision-making.

Leads data science for AML watchlist screening, developing multilingual NLP, entity resolution, information extraction, matching, risk-scoring, and graph-based detection systems. Builds data quality pipelines, evaluation frameworks, threshold tuning, backtesting, and production ML capabilities. Partners with Product and Engineering to deploy research at scale, leads technical initiatives, mentors peers, and translates model performance into compliance and business outcomes.

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.

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.

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.

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.

Lead data science efforts for federal customers: analyze large structured and unstructured datasets, build predictive models and ML algorithms, automate analytic workflows, mentor teammates, brief military leaders, translate technical requirements for non-technical stakeholders, and support deployments and CONUS/international travel as needed.

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.

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.