Top Senior Data Science Jobs

Leads the design and evolution of scalable statistical computing platforms, automation workflows, reusable programming tools, and data visualization capabilities for clinical development. Provides technical strategy, architecture guidance, regulatory-aware solutions, and leadership across global, cross-functional initiatives. Advises senior stakeholders, drives standards and tool adoption, resolves complex scientific computing challenges, and mentors teams. The role supports reproducible, compliant, high-performance evidence generation using Python, R, and advanced data science practices.

Build predictive and cohort models supporting underwriting and marketing. Source, evaluate, combine, and analyze internal and external data; develop analytical datasets and solutions; implement and monitor models in production; communicate insights to business partners; document methodologies and results; and support statistical analysis requests. May supervise 1–2 analysts and provide guidance on cohort modeling and related analytics techniques.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Senior Data Scientist partnering with Product, Engineering, and Design teams to analyze user behavior, product performance, and marketplace dynamics. Responsibilities include developing metrics and reporting frameworks, designing experiments, diagnosing business trends, analyzing marketplace behavior, and aligning stakeholders on analytical priorities. The role uses SQL and Python alongside BigQuery, dbt Cloud, Airflow, Tableau, and Statsig, with a focus on product analytics, experimentation, and data-informed decision-making.

Develop machine learning and AI methods for cybersecurity products using large-scale network telemetry. Analyze anomalies and malicious behavior, engineer features, evaluate models on imbalanced and incomplete data, establish monitoring metrics, and conduct error analysis. Collaborate with threat researchers and software engineers to productionize solutions, research emerging techniques, communicate findings, document methodologies, and mentor data scientists.

Designs, builds, and sustains data architecture, pipelines, ETL processes, and decision-support products for USPACOM’s MRT-C baseline. Ensures compliance with DoD data standards, Azure requirements, classification controls, and cross-domain information sharing needs. Integrates and validates terrain data, conducts quality assurance, produces baseline reports, and supports resource proposal development. The role requires advanced computational, software development, data analysis, machine learning, and cloud technology expertise in a national security environment.

Develop AI tools, machine learning systems, predictive analytics, data visualizations, and large-scale Multi-INT data fusion capabilities for USCYBERCOM J2 intelligence operations. Analyze and merge disparate data using R, Python, and SQL; create Power BI and Tableau visualizations; produce analytic reports; and collaborate with intelligence stakeholders to prioritize capability gaps.

Analyze complex structured and unstructured data; design data pipelines; develop predictive, prescriptive, NLP, clustering, forecasting, and machine learning models; automate data processing; use cloud and distributed computing platforms; create visualizations; collaborate with data engineers; research algorithms; and communicate insights to nontechnical audiences supporting U.S. Special Operations Command.

Develop and lead image and video generative AI pipelines, including model fine-tuning, multimodal workflows, agentic orchestration, and visual quality evaluation. Collaborate with Creative, Product, and Engineering teams to deliver scalable AI solutions for product imagery, virtual try-on, and digital content. Evaluate emerging models and vendors, guide data curation, and help productionize research prototypes into cost-effective services and APIs.

Design and deploy deep learning systems for search ranking, personalized recommendations, geospatial search, and pickup-point selection. Translate business goals into ML objectives, lead offline and online evaluation, conduct A/B tests, and partner with engineering on production model serving and latency optimization. Own the production ML lifecycle, including monitoring, drift detection, retraining pipelines, and quality management across international markets.

The Sr. Data Scientist will design and optimize intelligent search solutions using Elasticsearch and GenAI technologies, improving data retrieval and search relevance for enterprise applications.

Develops scalable data science and analytic solutions using large, disparate datasets. Responsibilities include statistical and machine learning analysis, entity resolution, graph and relational analytics, data profiling, validation, dashboards, reusable workflows, and operational tooling. The role translates complex findings into decision-ready insights, collaborates with analysts and engineers, documents methodologies, and transitions prototypes into reliable capabilities for government mission requirements.

The Data Scientist will apply advanced statistics and machine learning to solve cybersecurity challenges, develop prototypes, and present findings. Collaboration with government and academia is key.

As a Senior Staff Data Scientist, you will lead data science initiatives, mentor teams, develop frameworks for experimentation, and collaborate across departments to drive decision-making through data insights.

The Senior Data Scientist will develop data pipelines for AI applications, optimize data structures, manage databases, and ensure data quality and security.

As a Data Scientist, you will analyze complex data sets, build machine learning models, and work in a team to provide solutions, ensuring data security and optimization.

Develop and deploy data science and machine learning solutions for predictive maintenance and asset reliability in manufacturing. Build failure prediction, anomaly detection, equipment health, and remaining-useful-life models using sensor, maintenance, and operational data. Support feature engineering, time-series analysis, model validation, monitoring, retraining, deployment, and production troubleshooting. Partner with operations, reliability, engineering, and data teams to translate analytical outputs into practical maintenance decisions and improve model quality and adoption.

Leads the strategy, roadmap, and delivery of enterprise data products, analytics platforms, and AI-enabled solutions. Translates business needs into product requirements, prioritizes backlogs, defines governance and success metrics, and partners with engineering and business teams. Drives adoption, data quality, compliance, scalability, and measurable business value across quality, regulatory, manufacturing, supply chain, and commercial functions.

Apply statistical and computational techniques to large complex datasets to develop, evaluate, and deploy analytical models and reproducible workflows. Clean, transform, and visualize data, communicate findings to technical and non-technical stakeholders, and translate mission requirements into operational data solutions.

Designs and develops advanced analytics, machine learning, generative AI, agentic AI, and RAG solutions. The role includes framing business problems, curating and evaluating data, building analytical models, optimizing retrieval workflows, monitoring model performance, and translating stakeholder needs into measurable outcomes. The scientist also evaluates emerging technologies, establishes data science standards, communicates recommendations, and collaborates with business partners and other data scientists.

As a Staff Data Scientist, you'll drive data-driven insights, mentor junior staff, and integrate data science tools with engineering teams. You'll lead critical projects and represent data science in various settings.

Develop advanced data science and AI/ML solutions using complex healthcare data. Apply statistical modeling, machine learning, NLP, and LLM techniques, including RAG and prompt optimization. Build production-ready workflows integrating models with APIs, data pipelines, enterprise applications, and cloud platforms. Analyze data quality and model performance, communicate findings to technical and executive audiences, and collaborate across product, engineering, clinical, technology, and business teams.

Senior Data Scientist responsible for analyzing intelligence knowledge-management and production data, building executive dashboards, and developing automated ETL/ELT pipelines. The role forecasts analytical production and workforce resourcing, defines KPIs with leadership, translates statistical models into executive decision aids, and ensures data integrity, auditability, and governance. Work includes Python or R, advanced SQL, BI platforms, metadata management, and API integrations with Jira, Git, and ServiceNow. The position requires TS/SCI clearance and an initial 3–6 month embed at DIA HQ.

The Data Scientist III will enhance cybersecurity operations using data science and machine learning techniques, developing analytical models, and integrating data from various sources to improve threat detection and monitoring.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.