Top Senior Data Science Jobs

Develops data-driven solutions by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Builds predictive models, algorithms, data pipelines, dashboards, and interactive visualizations using Palantir Foundry and Power BI. Works with clients and cross-functional teams to translate complex data into actionable insights and guide technical development. Requires experience with data science programming, database-driven applications, and classified environments.

Develops and deploys production machine learning models and data science solutions for business, legal, investigative, and regulatory needs. Designs AI evaluation frameworks assessing bias, accuracy, reliability, and performance; analyzes structured and unstructured data; builds pipelines and testing tools; and translates legal and business requirements into technical solutions. The role collaborates with lawyers, clients, and cross-functional teams, manages concurrent engagements, communicates insights, and supports revenue-generating professional-services delivery.

Develops and optimizes NLP and machine learning pipelines for medical and claims data supporting VA disability claims adjudication. Responsibilities include extracting and analyzing healthcare data, tuning transformer-based models, validating performance, documenting workflows, collaborating with VA stakeholders, and supporting model integration into government systems. The role requires Python, NLP, SQL, healthcare data knowledge, and experience delivering data science projects in regulated environments.

Develops machine learning, statistical, graph-based, and data-mining algorithms for national security analytics. Builds and validates descriptive, predictive, and prescriptive models; develops LLM and NLP solutions; creates visualizations and experiments; extracts and transforms data; collaborates with subject matter experts; and integrates prototype analytics into production analyst workflows. The role also guides analytic development efforts and requires an active TS/SCI clearance with polygraph.

Develop and operationalize Python-based data science solutions supporting DoD and Intelligence Community missions. Integrate structured and unstructured data from APIs and enterprise repositories, build analytic pipelines, dashboards, visualizations, and interfaces, and modernize legacy workflows. Analyze large datasets, create automated processes, architect scalable solutions, document workflows, and identify patterns to improve intelligence production, analytic rigor, transparency, and mission effectiveness.

Own the enterprise ontology and knowledge graph strategy across Gas Power domains. Establish OWL, RDF, SHACL, and SPARQL standards; resolve cross-domain modeling conflicts; scale training and delivery practices; govern model approval, versioning, lifecycle, access, metadata, and lineage; and ensure the knowledge graph supports GenAI, RAG, and agentic workflows. Serve as the final technical authority while mentoring teams and partnering with senior stakeholders on platform selection and roadmap decisions.

Evaluates and improves statistical models, machine learning algorithms, and analytic workflows supporting classified Space and Counterspace intelligence missions. Analyzes complex datasets, validates model performance, assesses data quality, recommends enhancements, integrates new analytic approaches, and prepares technical assessments for government stakeholders. The role collaborates with engineers, developers, and data science teams in a classified environment while ensuring security and data governance compliance.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing ETL pipelines, predictive models, machine learning algorithms, and automated analytic software. Collaborates with clients, guides teammates, and translates data into actionable insights. Requires Python or R, statistical and machine learning expertise, version control, TS/SCI clearance with polygraph eligibility, and a bachelor's degree. Preferred experience includes intelligence disciplines, deep learning, LLMs, DevOps, data visualization, and ontology development.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing predictive models, machine learning algorithms, ETL pipelines, automated analytic software, and data visualizations. Partners with clients to define needs, translates data into actionable insights, guides teammates, and supports solutions involving AI, IoT, and intelligence analysis. Requires extensive programming, statistical modeling, data engineering, and classified-environment experience.

Leads data science efforts by analyzing complex structured and unstructured datasets, developing algorithms, dashboards, visualizations, and lightweight applications, and translating findings into actionable client insights. Uses Python, SQL, ETL, APIs, and machine learning techniques while guiding teammates and communicating results to technical and nontechnical audiences. Requires TS/SCI clearance and a bachelor's degree in a quantitative or STEM field.

Leads data science initiatives by analyzing complex structured and unstructured data, developing algorithms, dashboards, visualizations, and lightweight applications, and translating findings into client insights. Uses Python, SQL, ETL, APIs, and machine learning techniques to solve problems across public and private sectors. Guides teammates, communicates results to technical and nontechnical audiences, and supports data-driven decision-making in classified environments.

Leads advanced analytics and machine learning initiatives, including predictive modeling, causal inference, recommendation systems, anomaly detection, forecasting, and optimization. Designs scalable batch and streaming data solutions, lakehouse architectures, data pipelines, and model lifecycle practices. Partners with stakeholders to translate insights into business outcomes, supports data governance and quality, and provides technical leadership and mentorship using Agile practices.

The Senior Data Scientist develops Python-based analytics services, machine learning applications, Streamlit GUIs, automated data-linking methods, and API integrations. Responsibilities include exploratory analysis of structured and unstructured classified and open-source data, predictive modeling, entity resolution, applied statistics, data-source evaluation, and full data-process lifecycle management. The role requires collaboration with stakeholders, advanced SQL and large-scale data systems experience, and support for complex intelligence data-science programs.

Develop client-facing analytics, dashboards, data models, and large-scale data workflows while building and operationalizing internal AI capabilities. Design and productionize LLM-powered and agentic workflows, support AWS-based AI infrastructure, and implement integrations, access controls, monitoring, security, and responsible-AI practices. Partner with IT and practice teams to translate requirements into scalable solutions, document runbooks, monitor adoption and performance, and improve secure production use.

Develop marketing effectiveness analytics using Marketing Mix Modeling, attribution, customer lifetime value, experimentation, and causal inference. Build automated, scalable model pipelines, insight-ready datasets, and self-serve dashboards. Analyze campaigns, support marketing strategy, communicate findings to executives, and lead high-impact projects independently while collaborating cross-functionally.

Own analytical strategy and develop AI/ML models for Zip’s financial products, including cash and rent advances. Lead ambiguous 0-to-1 initiatives using bank transaction data, establish evaluation frameworks, guide risk and growth decisions, and deliver models from experimentation through production. Provide technical direction to data scientists, mentor junior team members, collaborate with engineering, and advise senior stakeholders on model trade-offs. The role requires strong financial services, statistical, research, and machine learning expertise.

Builds high-throughput data pipelines and predictive analytics for large-scale defense datasets. Responsibilities include statistical modeling, data mining, pipeline orchestration, cloud data warehousing, data integration, and interactive decision-support dashboards. The role supports military leadership with technical portfolio planning and experimentation analysis, and may apply NLP, DevSecOps, DataOps, and MLOps practices.

Develop and execute AML transaction monitoring data strategy, build and monitor model performance metrics, investigate data quality issues, define transaction-monitoring specifications with business partners, and address open AML findings to ensure effective anti-financial crime controls.

Serve as the technical lead for data science across Ripple’s product and business teams. Define analytics strategy, create reusable measurement and scientific frameworks, apply AI and agentic workflows to accelerate insight generation, evaluate growth and adoption, communicate executive-level metrics, and mentor data scientists. Work involves experimentation, causal inference, forecasting, statistical modeling, and analysis of payments, stablecoin, custody, and blockchain-related data.

Perform exploratory, predictive, and diagnostic data analysis supporting Navy fraud analytics, audit readiness, internal controls, and decision-making. Develop data models, dashboards, visualizations, and AI/ML solutions; analyze data quality and anomalies; and support automated monitoring and governance. Collaborate with engineers, architects, analysts, and government stakeholders to gather requirements, validate data, identify trends, and communicate findings through analytical products and executive presentations.

Leads data science initiatives by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Develops predictive models, algorithms, quantitative analyses, and machine learning or NLP solutions to address client challenges. Collaborates with clients, provides data-driven recommendations, leads complex projects and deliverables, mentors team members, and identifies opportunities to apply data science to public- and private-sector problems.

Designs and deploys machine learning models, statistical analyses, data workflows, and custom algorithms for law enforcement, intelligence, investigative, and business datasets. Collects, cleans, processes, and analyzes large datasets; validates data quality and model performance; deploys predictive models; and presents actionable recommendations through visualizations to government stakeholders and executive leadership.

Lead strategic analytics across Go-To-Market and Account Management by building scalable analytical frameworks, diagnosing performance issues, developing dashboards and automations, and applying experimentation to identify causal relationships. Translate complex data into business strategy, drive measurable outcomes, and establish analytics best practices, terminology, and documentation across the organization.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

Develop machine learning and data science solutions for intelligence operations and special operations missions. Build geospatial and 3D computer models, select and tune machine learning algorithms, create predictive models, conduct quantitative analyses, and visualize data. Collaborate with clients and guide algorithm and system development using disparate data sources and modern data and computing tools.