Top Data Science Jobs

As a Data Scientist, you will design and develop data pipelines, perform data analysis, and collaborate with the product team to implement solutions for large datasets.

Design and develop data solutions and predictive models from large healthcare data sources. Perform ETL, statistical analysis, and machine learning to inform pharmacy, medical cost, risk scoring, and customer engagement use cases. Support deployment of insights, collaborate with teams, and use CI/CD and agile practices to deliver analytics solutions.

Perform research and analytical work to support targeting, access discovery, and mission-focused research. Analyze complex datasets, develop models, and produce visualizations. Apply computational mathematics and Python-based analytics, document methodologies, and collaborate with technical and mission teams to support operational objectives.

Apply statistical and computational techniques to large complex datasets to develop, evaluate, and deploy analytical models and reproducible workflows. Clean, transform, and visualize data, communicate findings to technical and non-technical stakeholders, and translate mission requirements into operational data solutions.

Support and manage Big Data platforms, analyze large mission datasets, develop and refine prototype algorithms, build automated data-processing workflows, create visualizations, and document methodologies while collaborating with technical and mission teams to deliver analytic solutions that support operational objectives.

Design and apply data science and machine learning methods to large, complex well-integrity datasets: data cleaning, modeling, validation, statistical analysis, visualization, KPI/dashboard creation, and presenting results. Support productization, business development, documentation, and research publication.

Lead people analytics initiatives by applying advanced data science and machine learning to HR data. Advise HR leaders, build analytics strategy and models, ensure data quality and governance, develop dashboards and automated solutions, translate findings into decision-ready insights, and support cross-functional data pipelines and measurement best practices.

Perform analytic modeling, statistical analysis, and data engineering to extract value from large, messy datasets. Design and implement ML and inference methods, translate mission needs into technical requirements, and communicate technical findings and recommendations to non-technical stakeholders.

Perform advanced data science R&D for intelligence/defense systems: collect and analyze large datasets, build and deploy production ML models, develop custom searches, improve analytic methodologies, and coordinate with internal and external partners to support mission goals.

Lead GTM-focused data science initiatives: build revenue forecasts, apply causal inference, develop AI-enabled sales and retention solutions, partner with Product/Growth/Commercial teams, and translate analyses into scalable, automated workflows and executive-ready recommendations.

Lead CRM marketing analytics for Seller and Owner verticals: build segmentation and personalization models, map customer journeys, design targeted campaigns, define KPIs, analyze consumer data, integrate CRM with other systems, collaborate with engineering and marketing to optimize engagement and drive revenue.

As a Staff Data Scientist, you'll drive data-driven insights, mentor junior staff, and integrate data science tools with engineering teams. You'll lead critical projects and represent data science in various settings.

Build models to predict infrastructure resource needs, analyze telemetry and usage to inform capacity planning, optimize compute provisioning and costs, collaborate with engineers and finance, and provide data-driven recommendations to improve infrastructure reliability and efficiency.

Design and deploy predictive forecasting and analytics to support Revenue operations. Build models for forecasting, churn, expansion, and pipeline analytics; create scalable dashboards; partner with Revenue Operations, Finance, and Data Engineering to improve data quality, KPI governance, and executive reporting to drive data-driven decisions and AI readiness.

Own end-to-end modeling for vendor matching, order routing, and supply-chain optimization. Build predictive, causal, RL and OR solutions; design autonomous decision logic; quantify counterfactual impact; ship production models with data engineering; mentor senior data scientists; write technical design docs; partner on quarterly priorities and expand into revenue cycle and finance domains.

Work with clients to analyze large structured and unstructured data sets, develop and evaluate AI/ML models, mentor teammates, and deliver actionable insights to leadership. Support DoW/IC customers and translate complex data into decision-ready products.

Build, deploy, and monitor predictive and descriptive models across the customer lifecycle. Conduct deep-dive analytics, develop BI to track program results, and partner with stakeholders to identify analytics-driven opportunities and solve business problems.

Join a talent network connecting senior/staff data scientists with VC-backed startups. Work with product and engineering to design experiments, build predictive and causal models, productionize analytics, create dashboards, mentor teammates, and shape data strategy to drive product and business outcomes.

Join a SignalFire talent network to be matched with VC-backed startups seeking Senior/Staff Data Scientists. Responsibilities include experimental design and analysis, predictive and causal modeling, productionizing models, building metrics and dashboards, advising product and business teams, mentoring other data scientists, and shaping data strategy and tooling at fast-moving startups.

Support data-driven decision-making by collecting, cleaning, and analyzing structured and unstructured data, performing EDA, assisting in building and validating ML and statistical models, creating visualizations and dashboards, documenting findings, and collaborating with cross-functional teams.

Lead analytics for Medicaid quality programs: build analytical frameworks, perform data analyses and causal inference to evaluate program impact, translate requirements into scalable data models, collaborate with cross-functional teams, and communicate findings and risks to stakeholders to drive quality improvements.

Apply advanced data science and ML techniques to national security problems: curate, transform, analyze large SIGINT/RF/PROFORMA datasets; build reproducible analytics, models, and visualizations; automate workflows and optimize data pipelines; communicate results to technical and non-technical stakeholders.

Develop ETL pipelines, datasets, dashboards, predictive and optimization models to support fleet planning, procurement, lifecycle and residual value analysis. Cleanse and analyze vehicle, supplier, telematics, maintenance, and financial data, communicate findings to stakeholders, and support cross-functional delivery and adoption of analytics solutions.

Client-facing data scientist who builds and optimizes fraud detection models and policies using large, multi-product datasets. Scope and deliver customer analytics projects, drive research and innovation, present insights to non-technical audiences, and collaborate with product and engineering teams. Use SQL, Python, LUA, BI tools, Git and Jira to create scalable solutions and become a subject-matter expert in cybercrime and industry-specific fraud patterns.

Design, build, and deploy predictive models for risk and fraud using R, Python, SAS, and SQL. Analyze large datasets, develop cloud-based analytic solutions, validate scoring code with stakeholders, and document statistical methodologies and best practices.