Top Data Science Jobs

Develop and deploy AI and data-driven systems for reactor servicing, focusing on RAG, LLMs, NLP, safety documentation, and operational procedures. Build Plotly Dash interfaces, optimize Python workflows, package and containerize applications, and collaborate with engineering and IT teams on reliable, compliant AI solutions. The role requires an active DOE L or DoD Secret clearance and supports up to 10% telework.

Develop and evaluate machine learning and statistical models supporting intelligence analysis. Curate, transform, and integrate data; build classifiers and anomaly detection systems; deploy and refine models in production; monitor model performance and data accuracy; collaborate with analysts and software developers; document processes and datasets; and communicate findings to stakeholders.

Support Computer Network Operations by applying AI and machine learning to network exploitation tools, data, and workflows. Evaluate large language models, analyze mission data using statistical methods, develop Python-based analytic and ML workflows, normalize and visualize data, and collaborate with operators, developers, and engineers to deliver mission-focused technical solutions.

Develops and deploys AI, machine learning, generative AI, and predictive analytics solutions for DHS clients. Responsibilities include building data and AI pipelines with Python, SQL, Spark, Databricks, and AWS; developing LLM, RAG, NLP, computer vision, and predictive models; evaluating model performance and responsible AI practices; creating dashboards and documentation; and supporting MLOps, DevSecOps, deployment, governance, and technical mentoring.

Build end-to-end data science, machine learning, forecasting, time-series, and AI solutions for supply chain and manufacturing operations. Develop analytical datasets, ETL/ELT workflows, dashboards, APIs, and production models using Python, SQL, cloud platforms, and modern data technologies. Partner with business and technical stakeholders to improve inventory, demand forecasting, delivery, production planning, cost, and operational efficiency. The role owns solutions from problem definition through deployment, monitoring, adoption, and continuous improvement.

Analyze complex datasets to identify trends, risks, and opportunities; translate findings into business recommendations; present models and insights to senior leadership; collaborate with stakeholders; and develop analytical models, dashboards, reporting tools, and forecasting methodologies.

Develops and manages predictive marketing models, including propensity, cross-sell, upsell, churn, and attribution models. Oversees data sources, model validation, recalibration, MLOps governance, monitoring, and lifecycle management. Delivers marketing analytics projects, improves workflows, applies emerging AI tools, and communicates data-driven recommendations to nontechnical stakeholders. Requires strong statistical modeling, programming, SQL, visualization, cloud data, and cross-functional decision-making skills.

Builds a talent network of experienced data scientists for potential future AI evaluation projects. Responsibilities may include creating grading criteria, evaluating AI-generated or human-produced data science work, writing evidence-based justifications, applying consistent judgment, and incorporating reviewer feedback. Candidates should have professional data science experience, strong Python and SQL skills, expertise in statistical modeling, machine learning, experimentation, causal inference, and excellent technical communication.

Lead product-focused data science for Cash App Lending: define measurement frameworks, run experiments, analyze customer and risk data, use AI tools to accelerate analysis, partner with product/engineering/risk, drive technical standards, and mentor junior data scientists to inform product and policy decisions.

Lead end-to-end data science for Cash App lending products: define measurement frameworks, run experiments, evaluate model and policy performance, detect bias/drift, build decision support, and mentor junior data scientists while partnering with product, engineering, and risk.

Lead end-to-end data science for Cash App Lending: define measurement frameworks, run experiments, evaluate model and policy performance, use AI tools to accelerate analysis, partner with product, engineering, and risk, and mentor junior data scientists to drive product and risk decisions.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Build and deploy AI and machine-learning solutions for clinical development, including evidence generation, trial design, simulation, decision support, and comparative evaluation. Develop reproducible model pipelines, integrate algorithms into production applications, and support compliant, interpretable ML architectures. Collaborate with product, engineering, UX, and scientific stakeholders to translate research problems into scalable data workflows and communicate modeling decisions clearly.

Develops end-to-end data science and AI solutions for supply chain and service operations. Responsibilities include building machine learning, GenAI, LLM, agentic AI, RAG, forecasting, and deep learning applications; deploying and monitoring models in production; designing GenAI architectures; analyzing operational data; partnering with stakeholders to improve forecasts and business decisions; and communicating insights to leadership.

Develop statistical and machine learning models for clinical trial enrollment, patient matching, forecasting, and trial management. Monitor model performance and bias, build scalable data pipelines using AWS services and dbt, and ensure compliance with HIPAA, GDPR, and privacy standards. Collaborate with ML engineering, product, and clinical research stakeholders to integrate models into practical healthcare solutions.

Analyze large customer datasets, evaluate machine-learning models, develop risk and fraud insights, and communicate data-driven recommendations to technical and nontechnical audiences. The role partners with sales and account teams throughout pre- and post-sales, creates automated analytical workflows, supports product optimization and upsell opportunities, and may build exploratory ML models. It also contributes thought leadership on identity verification, fraud prevention, model performance, and customer risk management.

Drive product-led growth through rigorous analysis of onboarding, activation, adoption, retention, expansion, and monetization. Partner with Product, Engineering, Design, Marketing, and Sales to design experiments, analyze funnels and cohorts, identify behavioral signals, build metrics and dashboards, improve instrumentation, and translate uncertain or incomplete data into actionable product recommendations.

Develop predictive, statistical, and machine learning models supporting Navy Reserve readiness, operational optimization, and mission assurance. Build analytics products, dashboards, and visualizations; collaborate with technical and government stakeholders; evaluate emerging AI/ML technologies; and communicate data-driven recommendations to technical and executive audiences.

Supports the U.S. Space Force by analyzing model-based systems engineering data, extracting and transforming structured and semi-structured information, and developing scripts, dashboards, visualizations, reports, and data collection frameworks. The role requires Python, Java, SQL, and dashboarding experience, collaboration with systems engineers, on-site work in El Segundo, and an active TS/SCI clearance with SAP eligibility.

Develop and deploy AI, machine learning, statistical, and adaptive process-control models for bioprocess optimization. Analyze sensor, batch, and experimental data; improve fermentation and downstream processing productivity, yield, cost, and scalability. Collaborate with engineers and scientists to integrate data science solutions into manufacturing and R&D operations, enhance data pipelines and modeling frameworks, and communicate findings to technical and non-technical stakeholders.

Lead customer implementations of enterprise AI and machine learning solutions for supply chain planning. Design, prototype, test, and deploy forecasting, demand sensing, inventory optimization, and predictive analytics models. Partner with enterprise clients and executives, mentor data science teams, apply MLOps best practices, and integrate custom models into the o9 platform. The role requires deep expertise in time-series forecasting, mathematical optimization, machine learning, and supply chain analytics.

Conduct exploratory data analysis, run and interpret A/B tests, define and track product metrics, and support machine learning feature analysis and model evaluation. Analyze user, content, and engagement data using Python, SQL, and PySpark; monitor model performance and identify anomalies. Collaborate with product, design, engineering, and data science partners, communicating findings and recommendations to inform product decisions.

Lead growth analytics across acquisition, activation, approval, signup, and retention. Partner with product, design, engineering, and marketing leaders to define metrics, size opportunities, prioritize roadmaps, analyze experiments, and deliver strategic recommendations. Build funnel, cohort, and retention analyses; collaborate on self-service metrics; and communicate evidence-based findings to technical teams and executives.

Conduct data science and Multi-INT analytics across complex intelligence datasets. Develop data pipelines, statistical analyses, predictive and machine learning models, AI-enabled applications, dashboards, and visualizations using R, Python, SQL, Power BI, and Tableau. Integrate structured and unstructured data, evaluate model performance, identify trends and anomalies, and communicate analytical findings to technical and mission stakeholders. The position requires a TS/SCI clearance and may involve deployment for up to six months annually.

Build scalable cybersecurity data pipelines and analytic workflows supporting AI-enabled threat detection, anomaly detection, automated triage, and real-time Cyber Fusion Center operations. Use Python, SQL, Apache Spark, Databricks, ETL, and version control to deliver production-ready solutions. Collaborate with cyber operators and AI engineers to prioritize datasets, shape requirements, and enable autonomous cyber defense capabilities.