Top Data Science Jobs

Develops and applies statistical, geospatial, and data science methods to support GEOINT processes. Responsibilities include automating geodatabase workflows with Python and ESRI tools, extracting and validating geospatial data, performing statistical analysis, resolving entities across disparate sources, creating visualizations and dashboards, publishing GEOINT web services, and developing applications. The role supports classified government customers and requires an active TS/SCI clearance with CI Polygraph.

Develops data science solutions by exploring, cleaning, analyzing, visualizing, and modeling structured and unstructured data. Works with clients to define analytical needs, develops algorithms and machine learning systems, leads complex solution development, and translates data into actionable insights. Potential applications include fraud detection, cancer research, and national intelligence. The role may involve NLP, text mining, AI engineering, LLMs, distributed computing, and production machine learning pipelines.

Analyzes structured and unstructured financial and ERP data using Python, SAS, statistical methods, predictive modeling, and advanced algorithms. Supports Oracle E-Business Suite financial processes, data conversion, reporting, and continuity of operations planning. Develops models and actionable insights for business decisions, collaborates with engineering and disaster recovery teams, maintains contingency plans, and executes recovery exercises. Requires a master’s degree, 5+ years of experience, Oracle expertise, certification eligibility, and approximately 25% annual travel to Washington, DC.

Build and operate AI-assisted data platforms using data preparation, knowledge graphs, semantic technologies, machine learning, and AI agents. Profile and reconcile enterprise data, create metadata and evaluation sets, assess language-model workflows, analyze human-review feedback, and improve platform performance. Partner with engineering teams to deploy validated solutions, maintain reproducible documentation, and communicate findings to technical and mission stakeholders. Handle sensitive data under governance and audit controls while supporting solution planning and proposals.

Build and productionize data pipelines and LLM-based, multimodal AI solutions for insurance use cases. Develop agentic AI applications using RAG, embeddings, prompting, custom tools, and evaluation frameworks. Apply responsible-AI practices, deploy and monitor machine learning systems, and lead client workshops to translate business needs into technical solutions and roadmap priorities.

Develop, deploy, and monitor predictive models supporting Basketball Operations decision-making. Build model features, analyze large datasets, identify trends, and collaborate with data engineering and analysts to integrate results into organizational decisions. The role requires Python, predictive modeling, SQL, cloud, and production analytics expertise, along with strong communication and presentation skills. This is a full-time remote position with a flexible schedule that includes nights, weekends, and holidays.

Develop and lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Conduct CBRN operational impact analyses, evaluate scientific test results, develop hazard prediction and sortie-generation models, and create custom software and visualization tools. Support Air Force policy, doctrine, research, and strategic planning through technical reports, recommendations, briefings, and collaboration with defense, academic, and research communities.

Build production-grade AI applications for wealth and investment management, including portfolio analytics, investment research, decision support, data pipelines, APIs, retrieval systems, and agentic workflows. Apply quantitative and statistical methods to portfolio construction, risk analysis, and performance attribution while integrating financial data sources. Support model evaluation, monitoring, testing, documentation, code reviews, and scalable engineering standards across investment, product, and engineering teams.

Analyze and visualize complex RF, ELINT, and Technical SIGINT datasets supporting Advanced Weapons and Counter Proliferation missions. Develop data workflows, actionable intelligence products, visualizations, and analytical methods while validating data quality and documenting findings. Collaborate with engineers, analysts, and mission partners on RF technology identification, mission planning, and technical intelligence analysis.

Develop data models, algorithms, visualizations, workflows, and systems engineering documentation for an intelligence customer’s AI and risk management capabilities. Integrate and organize structured and unstructured data, validate requirements and controls, support cloud infrastructure in high-security environments, and translate business needs into technical solutions. Collaborate across sponsor organizations, conduct link analysis, identify technology gaps, and communicate technical findings through briefings and reports.

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.