Top Data Science Jobs

Lead the data science strategy for deployed humanoid robots: build predictive maintenance and reliability models, drive fleet performance and RaaS economics analysis, develop anomaly detection and RCA tooling, link manufacturing test data to field outcomes, and set instrumentation, experimentation, and data-quality standards across hardware, software, manufacturing, and operations.

The Data Scientist will enhance OpenAI's experimentation platform, improve statistical rigor, develop analytical systems in Python, and mentor other data scientists.

The Senior Data Scientist will develop analytical solutions for marketing and risk management, manage data sets, and support modeling activities, collaborating with teams to deliver actionable business insights.

Own complex data science projects applying machine learning, generative AI, statistical methods, and natural language processing to legal knowledge management and regulatory intelligence. Build systems for retrieval, summarization, classification, requirements extraction, impact analysis, and decision support. Establish data foundations, evaluation metrics, human-review workflows, and responsible AI practices while partnering with legal, policy, research, engineering, and business stakeholders. Mentor practitioners and operationalize scalable, auditable solutions.

Develop and deploy data science solutions for financial crime and fraud prevention. Responsibilities include understanding, cleaning, validating, and preprocessing client data; engineering features; tuning machine-learning models; optimizing runtime performance; communicating findings; collaborating with client stakeholders and internal teams; and improving data science processes, tooling, and best practices.

Develop and maintain advanced data processing, entity resolution, and data cleansing pipelines using Python and SQL. Responsibilities include NLP, information extraction, data validation, query optimization, testing, deployment, monitoring, code reviews, and maintaining data integrity, privacy, security, and reproducibility. The role requires expertise in record linkage, named entity recognition, text processing, matching strategies, and machine learning libraries, with on-site work at SSA headquarters in Woodlawn, Maryland.

Own the complete data science lifecycle for a flagship predictive modeling project. Responsibilities include analyzing and engineering multi-source data, building production-grade pipelines, developing and deploying supervised, unsupervised, reinforcement, deep learning, NLP, and forecasting models, monitoring performance, documenting work, communicating insights, and mentoring junior team members.

Lead evaluation and improvement of the U.S. Census Bureau’s LLM Autocoder for survey write-in responses. Establish performance baselines, design reproducible experiments, analyze coding accuracy and errors, compare LLM and rule-based methods, assess expansion to additional survey fields, and guide engineers on model and preprocessing changes. Collaborate with government subject matter experts, interpret pilot and production results, and document recommendations and reusable technical practices.

Design, develop, deploy, and optimize enterprise data science and machine learning solutions. Responsibilities include predictive modeling, statistical analysis, feature engineering, data exploration, ML pipeline deployment, model monitoring, and data-driven decision support. Collaborates with engineers, developers, product managers, business stakeholders, and cross-functional Agile teams using cloud platforms and large-scale data technologies.

Build and maintain data science models for a PRO loyalty program, including churn, retention, customer lifetime value, propensity, segmentation, reward redemption, and program economics. Design experiments, measure incrementality, identify at-risk members, and support loyalty and marketing decisions. Partner with Finance, Marketing, PRO, IT, and Data Engineering to translate analytical findings into actionable strategies, present results to stakeholders, and maintain reproducible model documentation.

Develop and maintain data pipelines and reproducible workflows for structured and unstructured Federal grants and financial data. Prepare datasets, features, documents, embeddings, and retrieval workflows for AI/ML and advanced analytics. Implement data quality, provenance, lineage, metadata, and traceability controls; troubleshoot integrations; document technical processes; and support secure Federal deployments while meeting governance, privacy, security, and accessibility requirements.

Senior Data Scientist partnering with Product, Design, Engineering, Finance, and Marketing to drive user growth through data science, causal inference, experimentation, and analytics. Responsibilities include developing LTV and retention models, designing A/B tests, creating measurement frameworks and dashboards, translating complex analyses into recommendations, and strengthening data foundations for scalable experimentation.

Lead complex AI and data science initiatives from problem framing through production delivery. Apply machine learning, optimization, reinforcement learning, forecasting, and agentic AI to operational and business challenges. Design safe, scalable, auditable AI systems with human oversight, improve model performance and cost efficiency, and advise stakeholders and colleagues on advanced AI adoption.

Leads data science efforts involving large-scale data analysis, machine learning, predictive modeling, NLP, and LLMs. Designs data-handling approaches, resolves storage and search challenges, develops software to improve data processes, validates data quality, and delivers actionable recommendations. Requires extensive experience with big data technologies, a related bachelor’s degree or equivalent experience, and an active Top Secret/SCI-eligible clearance with potential CI polygraph eligibility.

Lead data science projects to optimize drug development, develop AI models for clinical trials, and collaborate with teams on biomedical workflows.

Conduct hypothesis-driven analyses across claims portfolios, develop heuristic and predictive models, build scalable software tools, and communicate insights to technical and nontechnical stakeholders. The role collaborates with customer-facing, product, design, and sales teams; mentors junior data scientists; performs rigorous experiments; and continuously deploys maintainable, tested code to improve claims outcomes.

Lead data science across product and business areas, developing measurement frameworks, metrics, causal inference methods, forecasting models, and statistical analyses. Partner with product and business leaders on roadmap and growth decisions, apply AI and agentic workflows to accelerate analytics, communicate insights to senior stakeholders, and mentor other data scientists.

Build Copilot quality measurement and decision systems using customer, product, evaluation, and usage signals. Responsibilities include prioritization frameworks, taxonomies, representative evaluation portfolios, quality gates, scorecards, causal analysis, sampling, clustering, trend detection, and automated reporting pipelines. The role connects offline evaluations with online customer outcomes, identifies coverage gaps and failure patterns, supports product and model investment decisions, and communicates recommendations to technical, product, and executive stakeholders while maintaining privacy, provenance, reproducibility, and metric governance.

Leads advanced data science initiatives focused on marketing measurement, attribution, customer lifetime value, personalization, recommendations, experimentation, segmentation, and incrementality. Develops production-ready models and reusable analytics frameworks using large customer and behavioral datasets. Partners with marketing, engineering, technology, and leadership teams to translate analysis into actionable strategies, communicates insights clearly, mentors junior team members, and drives measurable business outcomes.

Leads data science and analytics initiatives supporting clinical development and precision medicine. Develops statistical methods, machine learning algorithms, prototypes, KPIs, reports, and predictive analyses. Partners with cross-functional teams to solve business problems, establish analytical standards, ensure regulatory and GCP compliance, and evaluate clinical trial progress and risks. Coaches junior team members and communicates technical insights to stakeholders.

Leads clinical development analytics and data science initiatives using statistical methods, machine learning, and AI. Collaborates with cross-functional teams to design analytical products, establish standards, develop KPIs and reports, identify clinical trial risks, and generate data-driven insights. Ensures compliance with GCP, ICH guidelines, regulations, and company SOPs. Coaches junior team members and communicates technical findings to stakeholders.

Leads data science and analytics initiatives supporting clinical development and R&D. Develops statistical methods, machine learning algorithms, prototypes, KPIs, reports, and predictive analyses. Partners with cross-functional teams to identify business needs, evaluate clinical trial progress, manage risks, and deliver standardized analytical solutions. Ensures compliance with GCP, ICH guidelines, regulations, and company SOPs while mentoring junior team members.

Leads clinical development analytics and data science initiatives using statistical methods, machine learning, and AI. Designs analytical standards, prototypes, KPIs, reports, and predictive models; ensures compliance with GCP, ICH guidelines, regulations, and company procedures. Partners with cross-functional teams as an analytics consultant, supports precision medicine and clinical trial risk evaluation, and mentors junior data scientists.

Lead Data Scientist partners with business teams to solve problems using data: pull and analyze large datasets, build and deploy statistical and machine learning models, run experiments and causal inference, create dashboards, automate workflows, and mentor junior scientists. Works with data engineers to operationalize scalable AI solutions and communicates insights to non-technical stakeholders to improve efficiency, reduce costs, and enhance member experience.

Build and own the analytics pipeline and semantic layer, define canonical product and business metrics, evaluate AI/agent performance, support pricing and profitability analysis, design dashboards and quality checks, translate ambiguous questions into analyses, and hire/scale the data team.