Top Data Science Jobs

As a Data Scientist, you'll analyze complex data to extract insights and guide clients' decisions through statistical methods and machine learning techniques while leading teams on missions that require deep data solutions.

Develop and lead data science solutions: build ETL pipelines, clean and model structured/unstructured data, design algorithms and automated analytics, produce visualizations, use ML methods, collaborate with clients and mentor teammates to turn data into actionable insights for government and commercial problems.

Work with clients to formulate questions, explore and clean structured and unstructured data, build predictive models and algorithms, develop automated analytic software and ETL pipelines, and create visualizations and insights to support decision-making in intelligence and commercial contexts.

The Principal Plasma Data Scientist will develop and validate machine learning frameworks for plasma modeling, integrating these models with experimental workflows and collaborating with experts to enhance modeling accuracy.

Prepare and analyze structured insurance data, perform EDA, and build/validate predictive models for risk, claims, pricing, and fraud. Collaborate with senior data scientists and stakeholders, ensure model transparency and regulatory compliance, communicate insights, and support improvements to data pipelines and analytical processes.

Develop, deploy, and maintain predictive models and perform advanced analysis to generate actionable insights. Manage model lifecycle (validation, monitoring, retraining), collaborate with engineers and business partners, design experiments, document processes, and mentor junior data scientists while promoting analytical best practices.

Build and scale data science products for marketing measurement. Design econometric and causal models, estimate price/incentive elasticities, develop predictive models, partner with engineers for feature sourcing and productionization, communicate results to stakeholders, and uphold MLOps and model quality best practices.

Build models, heuristics, and analytics that optimize model/provider routing and user experience for a large LLM marketplace. Partner with product and engineering to design user-facing data features, define product metrics, run deep analyses, and ship data-driven improvements tied to roadmap decisions. Scale analytical foundation in ClickHouse/dbt.

Analyze large structured and unstructured datasets, develop and deploy predictive models, create dashboards and visualizations, collaborate with stakeholders, ensure data quality, automate pipelines, and monitor model performance to drive business and sustainability decisions.

Design measurement frameworks and advanced causal inference solutions for marketplace allocation and recommendation systems. Partner with Product, Engineering, and business stakeholders to translate needs into scalable data science tools, run experiments, quantify impact, and drive implementation for Netflix discovery surfaces.

Lead marketing analytics and modeling to grow lending business: design experiments, build and validate campaign pipelines, measure funnel and ROI, manage direct-mail campaign selection and execution, create dashboards and provide data-driven recommendations to marketing and senior leadership.

Support IRS projects by developing, validating, and deploying predictive and statistical models on large tax and financial datasets. Perform data cleaning, feature engineering, anomaly detection, and risk scoring using Python/R, SQL, and Databricks. Communicate results to stakeholders and collaborate with cross-functional teams to inform audit prioritization, fraud detection, and compliance efforts.

Design, develop, validate, and deploy statistical and machine learning models to detect fraud, anomalies, and noncompliance in large tax and financial datasets. Clean and feature-engineer data, run robustness and sensitivity tests, collaborate with stakeholders, and present technical and non-technical findings. Support end-to-end data science lifecycle and productionization in a government environment.

Lead and support full-lifecycle data science projects (forecasting, NLP, classification/regression, optimization). Design production-ready solutions within client environments, consult with stakeholders to define ambiguous problems, communicate technical findings, and develop/teach bespoke analytics training.

Support IRS tax compliance and fraud detection by developing, validating, and deploying predictive and statistical models. Clean and analyze large datasets, apply supervised and unsupervised learning (including NLP and graph analytics), collaborate with clients and teams, and deliver technical briefings and defensible, explainable model results.

Work with clients and technical staff to design, develop, and deploy predictive models and data-driven analytical solutions. Perform data extraction, ingestion, and modeling; evaluate and improve models; translate business rules into technical specifications and communicate results to stakeholders.

Support IRS analytics to detect fraud and non-compliance by developing, validating, and deploying predictive and statistical models. Clean and analyze large datasets, perform feature engineering, apply supervised and unsupervised techniques (including NLP and graph analytics), collaborate with stakeholders, and deliver technical and non-technical briefings. Work across the full data science lifecycle and operate in secure government environments.

Develop, validate, and deploy predictive and statistical models using structured and unstructured tax and financial data to detect fraud and non-compliance. Clean and wrangle large datasets, perform feature engineering, apply supervised and unsupervised techniques, and communicate results to technical and non-technical stakeholders. Collaborate with clients and cross-functional teams and support model deployment and documentation in secure government environments.

The Data Scientist Level 4 develops strategies to extract insights from large datasets, utilizing advanced analytics and machine learning techniques. Responsibilities include statistical analysis, modeling, and effectively communicating findings to non-technical audiences.

Develop and validate AI modules to evaluate customer safety and risk data, perform statistical and causal analysis, debug AI outputs, write and maintain SQL for ETL, conduct ad-hoc analyses to inform decisions, and build customer-facing live risk dashboards and visualizations.

The Lead Data Scientist will manage projects on credit risk and business intelligence analytics, leveraging statistical models to provide insights for financial services clients. Responsibilities include developing solutions, leading client engagements, and mentoring junior staff.

Design, develop, validate, operationalize, and maintain large-scale data models and analytics. Perform end-to-end analytics including data wrangling, feature engineering, modeling (including NLP and ML), MLOps, and stakeholder communication to drive business insights and improve operational efficiency.

Lead data science projects: explore, clean, analyze structured and unstructured data; develop predictive models and visualizations; guide teammates and work with clients to deliver data-driven solutions for intelligence and public/private sector problems.

Use Palantir Foundry and ontology tools to ingest, transform, and analyze structured and unstructured data. Develop algorithms, applications, and workflows, ensure data governance and integrity, collaborate with clients and stakeholders, and present actionable insights to inform decisions.

Senior Data Scientist supporting Intelligence Community operations. Build customized visualizations and analytic applications using Esri and Python, automate analytic workflows, apply AI/ML to structured and unstructured data, and clearly communicate technical results to stakeholders.