Top Data Science Jobs

Build and deploy AI and machine-learning solutions for clinical development, including evidence generation, trial design, simulation, decision support, and comparative evaluation. Develop reproducible model pipelines, integrate algorithms into production applications, and support compliant, interpretable ML architectures. Collaborate with product, engineering, UX, and scientific stakeholders to translate research problems into scalable data workflows and communicate modeling decisions clearly.

Develops end-to-end data science and AI solutions for supply chain and service operations. Responsibilities include building machine learning, GenAI, LLM, agentic AI, RAG, forecasting, and deep learning applications; deploying and monitoring models in production; designing GenAI architectures; analyzing operational data; partnering with stakeholders to improve forecasts and business decisions; and communicating insights to leadership.

Develop statistical and machine learning models for clinical trial enrollment, patient matching, forecasting, and trial management. Monitor model performance and bias, build scalable data pipelines using AWS services and dbt, and ensure compliance with HIPAA, GDPR, and privacy standards. Collaborate with ML engineering, product, and clinical research stakeholders to integrate models into practical healthcare solutions.

Analyze large customer datasets, evaluate machine-learning models, develop risk and fraud insights, and communicate data-driven recommendations to technical and nontechnical audiences. The role partners with sales and account teams throughout pre- and post-sales, creates automated analytical workflows, supports product optimization and upsell opportunities, and may build exploratory ML models. It also contributes thought leadership on identity verification, fraud prevention, model performance, and customer risk management.

Drive product-led growth through rigorous analysis of onboarding, activation, adoption, retention, expansion, and monetization. Partner with Product, Engineering, Design, Marketing, and Sales to design experiments, analyze funnels and cohorts, identify behavioral signals, build metrics and dashboards, improve instrumentation, and translate uncertain or incomplete data into actionable product recommendations.

Develop predictive, statistical, and machine learning models supporting Navy Reserve readiness, operational optimization, and mission assurance. Build analytics products, dashboards, and visualizations; collaborate with technical and government stakeholders; evaluate emerging AI/ML technologies; and communicate data-driven recommendations to technical and executive audiences.

Supports the U.S. Space Force by analyzing model-based systems engineering data, extracting and transforming structured and semi-structured information, and developing scripts, dashboards, visualizations, reports, and data collection frameworks. The role requires Python, Java, SQL, and dashboarding experience, collaboration with systems engineers, on-site work in El Segundo, and an active TS/SCI clearance with SAP eligibility.

Develop and deploy AI, machine learning, statistical, and adaptive process-control models for bioprocess optimization. Analyze sensor, batch, and experimental data; improve fermentation and downstream processing productivity, yield, cost, and scalability. Collaborate with engineers and scientists to integrate data science solutions into manufacturing and R&D operations, enhance data pipelines and modeling frameworks, and communicate findings to technical and non-technical stakeholders.

Lead customer implementations of enterprise AI and machine learning solutions for supply chain planning. Design, prototype, test, and deploy forecasting, demand sensing, inventory optimization, and predictive analytics models. Partner with enterprise clients and executives, mentor data science teams, apply MLOps best practices, and integrate custom models into the o9 platform. The role requires deep expertise in time-series forecasting, mathematical optimization, machine learning, and supply chain analytics.

Conduct exploratory data analysis, run and interpret A/B tests, define and track product metrics, and support machine learning feature analysis and model evaluation. Analyze user, content, and engagement data using Python, SQL, and PySpark; monitor model performance and identify anomalies. Collaborate with product, design, engineering, and data science partners, communicating findings and recommendations to inform product decisions.

Lead growth analytics across acquisition, activation, approval, signup, and retention. Partner with product, design, engineering, and marketing leaders to define metrics, size opportunities, prioritize roadmaps, analyze experiments, and deliver strategic recommendations. Build funnel, cohort, and retention analyses; collaborate on self-service metrics; and communicate evidence-based findings to technical teams and executives.

Conduct data science and Multi-INT analytics across complex intelligence datasets. Develop data pipelines, statistical analyses, predictive and machine learning models, AI-enabled applications, dashboards, and visualizations using R, Python, SQL, Power BI, and Tableau. Integrate structured and unstructured data, evaluate model performance, identify trends and anomalies, and communicate analytical findings to technical and mission stakeholders. The position requires a TS/SCI clearance and may involve deployment for up to six months annually.

Build scalable cybersecurity data pipelines and analytic workflows supporting AI-enabled threat detection, anomaly detection, automated triage, and real-time Cyber Fusion Center operations. Use Python, SQL, Apache Spark, Databricks, ETL, and version control to deliver production-ready solutions. Collaborate with cyber operators and AI engineers to prioritize datasets, shape requirements, and enable autonomous cyber defense capabilities.

Lead product and program analytics for Microsoft skilling experiences: design learner journey/funnel analyses, define success metrics, run experiments and causal analyses, build proficiency/competency models, shape telemetry and scalable reporting, recommend content and curriculum improvements, and mentor other data scientists to drive product decisions and learning outcomes.

Own data science at a media and entertainment startup by acquiring unconventional data, analyzing cultural and audience trends, building production ML models for virality and content analysis, designing experiments, and delivering trusted dashboards and insights that guide business decisions.

Leads predictive modeling and machine learning initiatives to solve business problems and improve member experiences. Provides methodology guidance, coaches junior data scientists, develops and optimizes models, processes massive structured and unstructured datasets, manages batch and real-time scoring, and communicates findings to business partners. The role requires advanced statistical modeling, distributed computing, cloud analytics, deep learning, data engineering, and healthcare data analysis involving medical records, claims, and eligibility data.

Designs, deploys, and improves production machine learning and statistical models for identity resolution, entity matching, measurement, and analytics. Responsibilities include transforming large datasets, engineering features, developing evaluation and experimentation frameworks, monitoring deployed systems, communicating recommendations, partnering with product and engineering, and mentoring data science colleagues.

Develops scalable self-service analytics, data science frameworks, predictive and causal models, experimentation strategies, semantic layers, and AI-powered analytics tools. Partners with Product, Engineering, Design, and data engineering teams to improve product adoption, engagement, retention, and monetization insights. Leads complex analytics initiatives, ensures analytical rigor and governance, communicates findings to executives, and mentors analysts and data scientists.

Develop and deploy machine learning models for manufacturing use cases including predictive quality, machine failure prediction, and time-series pattern discovery. Work directly with customers to scope problems, validate solutions in live deployments, and translate successful analyses into product features. Collaborate with engineering, product, and customer success teams while applying statistics, optimization, generative AI, and agentic AI to deliver end-to-end solutions.

Build end-to-end data engineering and data science solutions for robotics and manufacturing data. Responsibilities include designing scalable multimodal data pipelines, creating analytic datasets, applying statistical and machine learning methods, developing MLOps workflows, building APIs and internal tools, and improving data governance, reliability, automation, and production support. The role partners cross-functionally with robotics, manufacturing, software, and data teams.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

Build LLM-powered research and analysis products for political strategists. Partner with users to shape workflows, define quality standards, design evaluations and LLM-as-judge systems, prototype features, and communicate technical tradeoffs to technical and nontechnical stakeholders. The role requires strong Python, SQL, statistical and machine learning skills, hands-on LLM and embedding experience, product judgment, and comfort with ambiguous problems.

As a Data Scientist at Opendoor, you'll enhance pricing strategies through statistical models, machine learning, experiments, and interdisciplinary collaboration to drive business outcomes.

Design and analyze experiments, causal inference studies, predictive models, and machine learning solutions. Evaluate AI and LLM-based systems, establish metrics and measurement frameworks, quantify uncertainty, and communicate insights through visualizations and data storytelling. Partner with engineering, product, and business teams to improve operational effectiveness and promote advanced analytics and experimentation practices.

Develop and implement data science models and algorithms, analyze complex datasets, ensure data quality and integrity, gather stakeholder data requirements, optimize data processes, and perform advanced statistical analysis and reporting in support of business needs.