Top Senior Data Science Jobs

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.

Lead end-to-end development and validation of statistical and machine learning models for media measurement. Analyze large datasets, conduct advanced statistical research, collaborate with engineering and product teams on scalable model deployment, and communicate insights through visualizations and presentations. The role requires strong expertise in statistical methods, survey design, sampling, predictive modeling, machine learning, and Python, with SQL and Spark experience preferred.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Leads rigorous evaluation methodologies for autonomous vehicle performance using statistical analysis, machine learning, simulation, and on-road data. Develops metrics, detects drift and anomalies, identifies edge cases, correlates simulation with fleet data, and strengthens launch-readiness and safety decisions. Partners with engineering, safety, and operations teams, communicates findings to leadership, establishes developer self-service tools, and mentors colleagues.

Develop and implement data science, predictive modeling, machine learning, simulation, and data engineering solutions for laboratory automation and digital R&D products. Lead pilots from opportunity identification through adoption, collaborate with scientists and technology teams, improve laboratory data quality and usability, integrate models into workflows, and measure business impact. The role requires technical leadership across multidisciplinary teams and contributes to the long-term Digital Lab roadmap.

Lead marketing analytics and modeling to grow lending business: design experiments, build and validate campaign pipelines, measure funnel and ROI, manage direct-mail campaign selection and execution, create dashboards and provide data-driven recommendations to marketing and senior leadership.

Use data science to source, ingest, clean, transform, analyze, and visualize structured and unstructured data in WDP. Develop dashboards and data products (Databricks, SQL, Python, Tableau/Qlik), create visual narratives, and brief senior DoW leadership to support IT portfolio management.

Leads applied AI/ML research as Principal Investigator and technical leader. Defines research portfolios, experiments, evaluation plans, and transition criteria; remains hands-on with prototypes, models, code, and end-to-end AI architectures. Leads cross-functional teams, mentors technical staff, evaluates emerging technologies, and transitions successful research into secure, scalable production capabilities using software engineering and MLOps practices. Communicates technical strategies to customers, business leaders, and engineering teams.

Develop predictive sales analytics products using machine learning, statistical modeling, optimization, and advanced analytics. Aggregate, normalize, and process large, inconsistent datasets to generate actionable insights. Apply supervised and unsupervised learning, boosting, ensemble methods, and empirical research. Collaborate with teams to create innovative products and bring them to market, using tools such as Python, R, SQL, Hadoop, cloud platforms, and visualization software.

Analyze large datasets using data science, machine learning, predictive modeling, and big data technologies. Design approaches for data handling, search, retention, storage, and processing; develop software to improve data searches; validate data quality and processes; and report actionable findings. The role emphasizes scientific data work over visualization and requires an active Top Secret/SCI-eligible clearance with potential CI polygraph eligibility.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

Develops data analytics and machine learning solutions for vehicle diagnostics, prognostics, preventive maintenance, and intelligent controls. Processes high-volume engineering and service data using Hadoop, cloud systems, and modeling tools; identifies patterns, anomalies, and trends; supports validation, product improvement, research proposals, and analytics strategy. Collaborates with data engineering, software, engineering, suppliers, and business teams while guiding complex projects and technical roadmaps.

Develop causal inference, experimentation, predictive, uplift, and treatment-effect models to measure business impact and improve targeting. Design and analyze A/B tests, work with large datasets, build scalable analytical frameworks and pipelines, and communicate actionable recommendations to technical and business stakeholders.

Develop and deploy data science and AI/ML solutions for scientific, engineering, and business problems within R&D. Responsibilities include data preparation, statistical modeling, machine learning, deep learning, computer vision, generative AI, LLM evaluation, predictive modeling, visualization, and pipeline development. The role collaborates with data engineers, scientists, domain experts, and stakeholders to scale AI solutions and communicate technical findings.

Build and validate analytical models using large structured datasets to improve operational and business outcomes. Translate complex findings into actionable recommendations, partner with engineering, design, and product teams to deploy production-ready solutions, lead high-impact federal projects, mentor junior staff, and shape data science strategy for Department of Education programs.

Develop, deploy, and monitor fraud detection models and pattern-recognition pipelines using Python, SQL, machine learning, and large transactional datasets. Partner with Fraud Operations to refine models, reduce false positives and negatives, analyze emerging fraud trends, and improve underwriting and verification strategies. Communicate insights to senior stakeholders, apply AI in production, and lead team members and initiatives.

Lead end-to-end development and deployment of predictive models, conduct advanced data-driven research, and solve ambiguous business problems. Partner with senior stakeholders and engineering leaders to shape strategic planning, resource allocation, and product innovation. Communicate complex findings to technical and executive audiences, architect scalable analytical solutions, mentor data scientists, and establish high standards for data science practices.

Develops advanced analytics and AI-driven solutions for CVS Health’s commercial product strategy. Translates healthcare and customer data into insights supporting product design, pricing, go-to-market execution, and performance optimization. Partners with Product, Sales, Actuarial, Marketing, and Underwriting leaders to identify high-value opportunities and deliver measurable financial outcomes. The role also involves stakeholder influence, experimentation, forecasting, product lifecycle support, and mentoring junior data scientists.

Leads data science, advanced analytics, dashboard development, data visualization, process automation, and metric governance for technology and enterprise operations risk. Develops descriptive, diagnostic, and predictive reporting; validates data quality, KRIs, KPIs, controls, and models; partners with stakeholders to deliver actionable insights. Uses SQL, Python, Power BI, Copilot tools, and Power Automate to improve reporting and operational efficiency while maintaining regulatory and internal control standards.

Leads advanced analytics, data visualization, dashboard development, reporting, metric governance, data quality, and process automation for technology and enterprise risk management. Uses SQL, Python, Power BI, Copilot, and Power Automate to create actionable insights, validate KRIs and KPIs, automate reporting, and improve operational efficiency. Partners with stakeholders, supports self-service BI adoption, maintains internal controls, and escalates risk-related issues. The role also requires dashboard standards, data modeling, visualization storytelling, and limited people leadership.

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

Design, develop, deploy, and monitor advanced deep learning models for fraud detection, risk management, and identity verification. Lead machine learning initiatives, research novel algorithms, manage end-to-end model lifecycles, collaborate with Product, Engineering, and Risk teams, and communicate insights to technical and executive stakeholders. Mentor data scientists and drive high-impact delivery, data quality, experimentation, and continuous improvement.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Lead end-to-end AI/ML initiatives that improve student retention, engagement, enrollment, and re-engagement. Build and deploy predictive models, next-best-action logic, and production MLOps workflows using Databricks, MLflow, dbt, and Dagster. Own experimentation, monitoring, performance measurement, cross-functional implementation, vendor coordination, and executive communication. Mentor teammates and drive measurable outcomes across Product, Engineering, CX, and university partners.