Top Data Science Jobs

Develop machine learning, statistical, data mining, graph-based, and heuristic algorithms for national security applications. Build and optimize AI/ML and large language models, including information retrieval workflows. Analyze varied datasets, create feature vectors, design experiments, evaluate models using standard metrics, produce visualizations, and translate subject matter expertise into production analytics. Guide analytic development efforts and integrate prototype algorithms into analyst workflows.

Leads data science and advanced analytics supporting Intelligence Community mission execution and national security objectives. Applies Python, R, Ruby, process automation, and large-scale data analysis to produce intelligence and targeting products. Collaborates with technical, executive, and government stakeholders, participates in peer reviews, and supports novel analytic methodologies. Oversees project operations, schedules, budgets, status reporting, issue resolution, and productivity goals. May lead teams through hiring, onboarding, performance management, and professional development.

Leads data science efforts supporting national security and Intelligence Community missions. Applies analytics and automation to large datasets, develops intelligence and targeting products, participates in advanced analytic peer reviews, and collaborates with technical, executive, and government stakeholders. Oversees project operations, schedules, budgets, status reporting, issue resolution, and productivity goals. May lead teams and support hiring, onboarding, and performance development.

Conduct workforce analytics and applied organizational research on attrition, mobility, engagement, performance, and planning. Analyze and validate HR data using SQL, R, or Python; apply statistical testing and regression; translate business questions into actionable insights; and communicate findings through visualizations, reports, and presentations. Partner with HR stakeholders while maintaining the accuracy, documentation, confidentiality, and integrity of sensitive workforce data.

Develops machine learning, statistical, data mining, graph-based, and large language models for national security applications. Analyzes datasets, creates feature vectors, prototypes and validates algorithms, builds experiments, generates visualizations, and implements analytics in production frameworks. The role collaborates with subject matter experts, optimizes GPU-based model training, supports LLM and RAG solutions, and guides analytic development efforts. An active TS/SCI clearance with polygraph is required.

Entry-level Data Scientist who will frame business and technical problems, prepare and validate data, build and evaluate statistical and machine-learning models, and communicate findings through visualizations and presentations. The role includes collaborating with engineering and product teams to develop reusable, monitored analytical solutions, maintaining reproducible workflows, and addressing data governance, cybersecurity, bias, and Responsible AI requirements. Candidates will develop expertise in energy, engineering, and industrial applications through practical projects and mentorship.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing algorithms, processing data, creating visualizations and dashboards, and delivering data-driven insights to clients. Uses Python, SQL, ETL processes, APIs, and machine learning techniques while guiding teammates and communicating findings to technical and nontechnical audiences.

Develops advanced analytics for national security using large, disparate datasets. Responsibilities include designing data exploitation methods, researching sponsor inquiries, developing custom searches, applying classification, regression, clustering, and anomaly detection algorithms, and supporting threat hunting and malicious-activity detection. The role also involves statistical analysis, data visualization for nontechnical audiences, and prototyping data engineering pipelines.

Lead computational analysis for research on intrinsically disordered proteins and regions. Develop reproducible pipelines, integrate HDX-MS, interactomics, cryo-EM, and molecular-dynamics datasets, apply statistical and machine-learning methods, create visualization tools, support experimental design, and collaborate with structural biology, proteomics, and computational modeling teams. Contribute to publications, technical reports, and open computational resources.

Build Way’s data and intelligence layer across pricing, merchandising, personalization, forecasting, recommendations, and automation. Own data foundations, metrics, models, experiments, and production-ready datasets while partnering with Product and Engineering. Identify high-impact opportunities, ship measurable solutions, and establish rigorous, responsible data practices in a fast-moving startup environment.

Build Way’s data and intelligence layer from the ground up. Own data foundations, metrics, models, experiments, forecasting, recommendations, and decision systems across pricing, merchandising, personalization, and automation. Partner with Product and Engineering to transform ambiguous business problems into rigorous, production-ready solutions, while establishing reliable data products and responsible analytical practices.

Leads hands-on data science projects analyzing sensor, camera, and multimodal data to understand human movement and athletic performance. Applies machine learning, computer vision, signal processing, and statistical modeling; designs validated analytical workflows; builds reproducible software, datasets, and pipelines; coordinates technical execution; communicates limitations and tradeoffs; and mentors contributors across scientific and engineering teams.

Leads data science strategy across Digital Turbine’s mobile advertising marketplace. Designs experimentation frameworks, causal inference methodologies, and production machine learning models for bidding, audience segmentation, lifetime value, attribution, forecasting, and recommendations. Partners with engineering on deployment and monitoring, establishes scalable data science standards, mentors scientists, and communicates technical insights to executives. Requires extensive experience in data science or machine learning, advanced statistical expertise, cloud-scale data processing, and cross-functional technical leadership.

Develop scalable data science solutions for large structured and unstructured datasets supporting national security missions. Automate data processing, build machine learning and graph-based capabilities, and integrate LLMs into knowledge management workflows. Translate customer needs into analytical solutions, communicate findings through reports and visualizations, deploy models with engineering teams, and mentor junior data scientists. The role is full-time onsite in Reston, Virginia and requires an active TS/SCI clearance with CI polygraph.

Partners with engineering, product, and business teams to frame problems, perform exploratory analysis, build statistical and machine learning models, write production code, run experiments, analyze data, communicate findings, and support launch decisions.

Develop and deploy machine learning algorithms that improve real-time device identification and fraud detection. Own data science projects from experimentation through production, including supervised, semi-supervised, and unsupervised learning, real-time inference, training automation, exploratory analysis, and model-to-service integration. Contribute to engineering best practices, collaborate within a global remote team, and participate in a shared on-call rotation.

Develop and deploy traditional machine learning, GenAI, and NLP solutions for healthcare, insurance, and life sciences applications. Responsibilities include model research, validation, documentation, deployment, maintenance, experimentation, and collaboration with product, business development, ML engineering, and IT teams. The role also contributes to data science best practices, internal tooling, peer review, coaching, and translating client analytical needs into commercially viable products.

Perform quantitative analysis, develop decision-support visualizations, and build Python software for data pipeline operations on a geospatial intelligence program. Analyze EO and SAR imagery, process complex geospatial data, and support AI/ML systems through ETL, SQL/NoSQL databases, and data manipulation. The role requires prior overhead imagery experience and an active TS/SCI clearance, with desired experience in NLP, computer vision, automated collection, and statistical modeling.

Analyze digital payment adoption, engagement, transaction flows, and member behavior using large datasets. Build statistical and predictive models, dashboards, and visualizations with Python, SQL, Databricks, Power BI, or Tableau. Translate analytical findings into recommendations for product strategy, member experience, operational efficiency, economics, and risk, while presenting insights to technical and business stakeholders.

Develop algorithms, predictive models, quantitative analyses, and visualizations from structured and unstructured data. Analyze complex datasets, apply machine learning, AI, NLP, and text-mining techniques, and translate findings into actionable insights for clients across government and private-sector challenges.

Lead the design, development, deployment, and maintenance of predictive models and decision systems. Explore data sources, run experiments, prototype solutions, apply advanced analytics, and drive strategic business improvements. Communicate findings to senior stakeholders, own projects end to end, mentor junior data scientists, and contribute to coding standards and technical direction.

Applies statistical analysis, data processing, visualization, and modeling to complex structured and unstructured datasets. Develops data solutions using Python, R, SQL, and related technologies; creates dashboards and reports; maintains data integrity and governance; and collaborates with stakeholders on business or mission objectives. The role also requires cloud-native application deployment using Kubernetes, Docker or Podman, Helm, Git workflows, Linux, configuration management, and software release processes, with preferred experience in microservices, APIs, event-driven systems, PostgreSQL, Redis, and cloud-native architectures.

Analyze wafer-fabrication, equipment, sensor, metrology, and manufacturing data using statistical analysis, AI, and machine learning. Develop anomaly detection, yield prediction, root-cause, data pipeline, dashboard, and model-monitoring solutions. Integrate diverse manufacturing data sources, support experiments and process improvement, validate and deploy models, and communicate actionable engineering insights to technical and management stakeholders.

Builds dashboards, applications, automations, ETL integrations, predictive risk models, and AI/ML solutions for unstructured data. Performs data normalization, visualization, business intelligence analysis, and risk assessment improvement. Recommends data storage and integration solutions, creates Power BI documentation, and applies SQL, R, Python, and other data science tools to federal systems and public-sector projects.

Conduct statistical and analytical investigations using mathematical and computer simulation models, with emphasis on computational electromagnetic modeling for NSWC Crane. The role leads complex technical problem-solving, customer requirements translation, design and proposal reviews, technical presentations, engineering guidance, and project team leadership. Frequent collaboration with customers, partners, subcontractors, and suppliers is required. The position requires an active Secret clearance, TS/SCI eligibility, U.S. citizenship, and 10–20% travel.