Top Senior Data Science Jobs

Lead design and delivery of people analytics products using predictive models and language models. Build unified workforce data models, semantic layers, governance, continuous listening & survey frameworks, and enforce experimental and psychometric rigor to inform talent, retention, and team-performance decisions.

Support Defense Intelligence analysts by building data pipelines, ML models, and analytics for multi‑INT data. Perform data engineering, mining, predictive analysis, and deliver visualizations. Use Python, R, SQL, Power BI, and Tableau. Work requires active TS/SCI clearance and 8+ years of data science experience.

Senior Data Scientist supporting Intelligence Community operations. Build customized visualizations and analytic applications using Esri and Python, automate analytic workflows, apply AI/ML to structured and unstructured data, and clearly communicate technical results to stakeholders.

Lead experimentation and decision science initiatives from problem definition through recommendations. Design and analyze A/B tests, quasi-experiments, matched-market tests, and causal inference studies. Apply advanced statistics, regression, predictive modeling, machine learning, and big-data analysis using Python, R, SQL, and PySpark. Partner with stakeholders, communicate actionable findings, build reproducible workflows, and mentor analysts and junior data scientists.

Build, validate, deploy, monitor, and maintain predictive models and analytical reports. Translate findings for technical and non-technical stakeholders, promote data literacy, deliver training, collaborate on data infrastructure, apply advanced statistical and machine learning methods, and support analytical requests across mental health and crisis care programs.

The Data Scientist will collaborate with teams to shape product strategy using machine learning and econometric models on large datasets, measuring outcomes and designing data-driven solutions.

The Data Scientist will conduct data research, develop analytics models, ensure data quality, and communicate actionable insights to stakeholders. Responsibilities include statistical analysis, data mining, and collaboration with business experts to enhance decision-making.

Lead design and implementation of scalable workflows to transform large geospatial/intelligence data into mission-ready analytic products. Script in Python, produce operational visualizations, provide senior-level direction and QA, and collaborate across NGA and IC to support intelligence operations.

The Data Scientist will analyze data using machine learning and statistical methods to deliver insights, develop algorithms, and support customers with analytical discussions.

As the Staff Data Scientist, you will lead the data science function, driving strategy, mentoring peers, and shaping product direction through advanced analytics and experimentation.

Build and deploy credit risk models for acquisition and account management. Create datasets and features from traditional and non-traditional sources, collaborate with credit, product, and engineering partners, run experiments, and monitor models in production to enable responsible lending decisions.

Data Scientists develop and maintain AI software and systems, focusing on machine learning algorithms, model deployment, and data analysis for predictive maintenance and failure prediction.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Designs, develops, validates, implements, and monitors statistical and machine-learning solutions for insurance pricing, product development, and profitable growth. Owns analytical workstreams from problem definition through delivery, builds scalable workflows using Python and cloud technologies, collaborates with data and business teams, participates in model and code reviews, communicates recommendations to stakeholders, and provides technical guidance to less experienced data scientists.

Lead data science for Disney’s News & Entertainment Machine Learning Platform. Conduct exploratory analysis, design and evaluate A/B tests, define success metrics, assess model performance, and partner with ML engineers on features and drift detection. Translate behavioral and content insights into product recommendations and roadmap priorities. Analyze large-scale datasets using Python, SQL, and PySpark, communicate findings to technical and non-technical stakeholders, and support personalization products across Disney media brands.

Leads the design and production deployment of data pipelines, data models, analytical applications, and machine learning solutions for aerospace manufacturing. Partners with IT, engineering, manufacturing, cybersecurity, and business stakeholders to deliver reliable, secure, traceable, and scalable analytics. Develops models for anomaly detection, forecasting, optimization, scheduling, and engine performance; establishes technical standards, testing frameworks, documentation, and governance; troubleshoots production issues; and mentors junior team members.

Build, validate, deploy, monitor, and document machine learning models for real-time fraud detection and financial crime risk. Analyze large datasets, ensure data quality, collaborate with risk and engineering teams, and improve model deployment and observability. The role also supports model governance, production reliability, and innovative risk solutions using modern data tools and potentially generative AI.

Develop and deploy machine learning models and analytical solutions from data exploration through production. Apply feature engineering, model selection, statistical analysis, and model evaluation while ensuring data quality, governance, and ethical analytics. Translate business problems into actionable insights, resolve technical challenges, and collaborate with engineering, product, operations, and stakeholders in cross-functional teams.

Partner with Product and Engineering to define product metrics, design experiments and causal analyses, build reporting and models, analyze user behavior and segments, and translate insights into product recommendations while establishing Product Data Science practices.

Lead the design, development, deployment, and maintenance of machine learning models for personalization, recommendations, churn prediction, lifetime value, advertising, and business insights. Own the full data science lifecycle, analyze complex datasets, collaborate with product, engineering, editorial, and revenue teams, mentor junior staff, and promote advanced data science practices.

Leads projects in data science to analyze complex data sets using advanced statistical methods, providing insights for business decisions and managing cross-functional teams.

Build and own production NLP enrichment pipelines for scientific literature at scale. Develop entity extraction, classification, claims, and summarization systems using classical NLP, embeddings, retrieval, LLMs, and fine-tuned models. Create evaluation datasets and metrics, balance quality, cost, and latency, and contribute to agentic AI applications. Collaborate with editors, product managers, engineers, SMEs, and vendors to deliver reliable modeling outcomes.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

The Data Scientist will develop tools for enhancing signature management capabilities, integrate diverse data holdings, and leverage AI/ML solutions for operational support.

Lead analytics for new-user activation and early-life product experiences. Design and analyze experiments, partner with product/engineering/design, build data foundations, and recommend product changes to improve user retention and habit formation.