Top Data Science Jobs

Lead experimentation, causal inference, advanced analytics, machine learning, and Generative AI initiatives. Design and analyze marketing and product experiments, build scalable predictive and prescriptive models, establish statistical governance, develop data pipelines, influence executive stakeholders, and mentor data scientists. Translate complex business challenges into measurable analytical solutions across marketing, product, sales, risk, and compliance functions.

Build and deploy NLP and machine learning models that analyze student and educator text. Develop classical ML and LLM-based features, production inference systems, validation and monitoring pipelines, data-drift detection, retraining workflows, and responsible AI guardrails. Partner with product, engineering, and data science teams to improve real-time education technology features while ensuring accuracy, fairness, privacy, and reliability.

Develop graph data models, optimize Neo4j and Cypher queries, build Python data parsers, validate mission data, and support near-real-time graph analysis. Translate analyst and customer requirements into graph-based solutions, collaborate with software developers, document schemas and processes, and address operational challenges through data modeling and graph technologies.

Build actuarial and health-economic models (total cost of care, PMPM, MLR) from claims to project multi-year cost and utilization impacts, quantify uncertainty, support commercial pricing, defend analyses to payer actuaries, partner on causal/expt design, and produce outputs usable for pricing/reserving.

Develop predictive models, customer segmentation, automated data pipelines, and analytics supporting CAHPS, HOS, and Medicare Advantage Stars strategy. Collaborate with engineering, clinical, compliance, coding, and legal teams to ensure CMS-aligned outputs. Analyze complex healthcare data, communicate insights to stakeholders, support dashboards and reporting, and help standardize AI-readiness workflows. The role requires advanced machine learning, programming, statistical analysis, and healthcare quality modeling expertise.

Analyze complex datasets and apply statistical, quantitative, qualitative, AI/ML, NLP, geospatial, and research methodologies to support national security missions. Develop automated workflows, models, dashboards, visualizations, and analytical tools; manage datasets and metadata; assess information environment and irregular warfare activities; and communicate actionable findings to operational leaders. Collaborate with intelligence, operations, research, and technology teams in classified environments.

Develops and supports data science and informatics solutions for DoD data, analytics, and AI initiatives. Responsibilities include building and testing machine learning models, analyzing large datasets, identifying patterns, creating data models, documenting requirements and findings, and presenting technical concepts to diverse audiences. The role requires experience with machine learning, programming, SQL, data visualization, terabyte-scale datasets, and GPUs, along with a Secret clearance and ability to obtain TS/SCI clearance.

Develop and maintain backend applications with a focus on NLP data preparation. Responsibilities include assessing and rewriting legacy Java applications, refactoring backend systems, building REST APIs, optimizing relational and non-relational database queries, and using testing frameworks to ensure application stability. The role requires extensive Java and Python experience, cloud and Linux expertise, and a TS/SCI clearance with polygraph.

Build custom reports and data products from client requirements through coding and ongoing delivery. Apply quantitative analysis, data mining, and research to generate strategic insights from raw datasets and algorithms. Partner with Engineering and Advisory Services teams to scale solutions and answer complex business questions. Independently manage projects while communicating with clients and stakeholders.

Perform data science, engineering, analytics, and statistical work for Department of War and Intelligence Community clients. Test and evaluate AI/ML models, collaborate with teams, communicate findings, and support or conduct all-source analysis. Experience with GEOINT and large language models is preferred. The role requires an active TS/SCI clearance and ability to pass a CI polygraph.

Conduct data science, engineering, and analytics work supporting Department of War and Intelligence Community clients. Test and evaluate AI/ML models, potentially work with large language models, perform all-source analysis, and collaborate with multidisciplinary teams. The role requires an active TS/SCI clearance with a recent CI polygraph and a bachelor’s degree or equivalent direct experience.

Develops and evaluates data science and AI/ML models while supporting Department of War and Intelligence Community clients. The role requires collaborating across teams, communicating effectively, conducting testing and evaluation, and potentially applying large language models and all-source analysis. Candidates must hold an active TS/SCI clearance with a current CI polygraph and have a bachelor’s degree or at least four years of direct data science experience.

Drive data-informed decisions across Product, Sales, and Engineering by analyzing usage patterns, business performance, and trends. Build data pipelines, write SQL and Python analyses, create Looker and Data Studio dashboards, monitor opportunities and risks, and enable teams to use analytics tools effectively.

The role involves analyzing fraud and risk data, defining KPIs, collaborating on anti-fraud strategies, and creating tools and dashboards for data accessibility at Whatnot.

Develop AI-driven planning, scheduling, resource allocation, and execution algorithms for autonomous spacecraft, vehicles, and mission operations. Apply search-based, constraint-based, satisfaction, optimization, geometric reasoning, and operations research techniques to complex mission data. Build software for automation, real-time decision-making, and scientific data analysis, while contributing to interdisciplinary teams and potentially leading development efforts.

Conduct machine learning and data science research for robotic exploration, science, and engineering applications. Develop algorithms, datasets, and models for airborne, orbital, and planetary mission data, lead small research teams, collaborate with scientific and engineering experts, publish findings, and support technology infusion across JPL and NASA. The role focuses on imaging spectroscopy, remote sensing, scientific validation, large-scale computing, and operational delivery of AI-driven technologies.

Serves as the senior technical authority for data science and analytics supporting an Intelligence Community program. Leads teams, assigns work, and oversees AI/ML models, data pipelines, predictive analytics, NLP, anomaly detection, and multi-source intelligence data fusion. Establishes validation and quality standards, integrates emerging AI/ML technologies, and ensures analytical outputs are accurate, reproducible, and defensible.

Build and productionize machine learning, NLP, generative AI, and analytics solutions for healthcare and regulated federal data. Responsibilities include data preparation, feature development, experimentation, model evaluation, error analysis, validation, workflow monitoring, troubleshooting, and translating stakeholder requirements into technical solutions.

Leads data engineering for the Sentinel program by developing, deploying, and maintaining production ETL/ELT pipelines from structured and unstructured sources. Oversees analytics data strategy, storage, governance, and quality; partners with stakeholders and visualization specialists; mentors junior engineers; conducts code reviews; and establishes engineering best practices for organization-wide analytics and decision-making.

Staff and Principal Product Data Scientists will use advanced analytics, experimentation, causal inference, machine learning, and large-scale data technologies to guide Slack product strategy. They will define success metrics, build self-service reporting and data models, evaluate product adoption drivers, identify growth opportunities, and help develop Slack’s agentic data delivery platform. The role partners closely with Product, Design, Engineering, Product Research, and go-to-market teams while communicating insights to executives and cross-functional stakeholders.

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 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 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.