Top Data Science Jobs

Lead end-to-end data science for Cash App Lending: define measurement frameworks, run experiments, evaluate model and policy performance, use AI tools to accelerate analysis, partner with product, engineering, and risk, and mentor junior data scientists to drive product and risk decisions.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Support Aftermarket digital products by gathering requirements, meeting customers, delivering training, developing data code, ensuring data quality/governance, testing, and analyzing large datasets to provide business insights and resolve technical product issues.

Leads complex analytics and AI consulting engagements from opportunity shaping through solution design, model development, validation, deployment readiness, and monitoring. Provides technical leadership across machine learning, generative AI, LLMs, RAG, MLOps, and responsible AI. Partners with clients and internal teams to deliver business value, communicates insights to technical and business stakeholders, contributes to innovation and governance, and mentors junior colleagues. Travel is required 10–20% of the time.

Leads fraud strategy and portfolio analytics initiatives using large structured and unstructured datasets. Develops machine learning algorithms, anomaly detection models, and statistical analyses to predict business outcomes and optimize products and processes. Directs data gathering, processing, mining, and analytical experiments, then presents insights through visualization and data storytelling. Collaborates with data architects, analysts, engineers, and visualization experts to deliver data-driven solutions.

Owns personalization data products and roadmaps, partnering with product, data science, analytics, and technology teams. The role defines requirements, prioritizes backlogs, leads initiative discovery, evaluates data availability and technical feasibility, and develops analyst-friendly data products. It applies descriptive, diagnostic, and inferential statistical analysis to uncover insights, improve customer experiences, and increase business value. Strong SQL, visualization, web analytics, communication, and stakeholder management skills are required.

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.

Deliver data-driven insights for Business Banking strategy using statistical analysis, predictive modeling, forecasting, text mining, Python, SQL, Snowflake, Databricks, and Tableau. Collaborate with senior stakeholders, manage large and diverse datasets, ensure data quality and security, support projects from requirements through delivery, and present actionable findings to senior leaders and investors.

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

The Senior/Staff Data Scientist will build and deploy predictive models for consumer decisioning and business optimization, focusing on machine learning and statistical modeling. Responsibilities include model development lifecycle, data exploration, collaboration with teams, and improving product outcomes.

New-graduate Data Scientist role building and deploying ML and GenAI systems at scale for cybersecurity. Responsibilities include model development, fine-tuning LLMs, embedding-based search, production-ready code, automated tests, architecture reviews, and cross-functional collaboration to deliver AI-powered security capabilities.

Perform advanced analytics and AI/ML to generate actionable insights for auto finance products. Build predictive models, measurement frameworks, and conversational AI solutions; partner with Product, Technology, and business leaders to drive growth, present findings, and refine KPIs and A/B experiment designs.

Apply statistical analysis, predictive modeling, experimentation, and AI-assisted methods to support product analytics for small business P&C insurance. Partner cross-functionally to build lead/trigger capabilities, measurement frameworks, and AI-enabled use cases to improve client outcomes and product performance.

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.

Develop and deploy predictive and descriptive models (including ML) across the customer lifecycle, perform deep-dive analytics, build BI to monitor program results, and partner with stakeholders to apply risk modeling and analytics to business problems.

Perform fraud analytics across the account lifecycle to identify risks and profitability drivers, develop monitoring and reporting, lead complex projects end-to-end, provide actionable recommendations, and communicate findings to stakeholders to guide fraud strategy and controls.

Lead fraud risk strategy using advanced analytics: develop and implement models and metrics, analyze account lifecycle profitability, drive cross-functional initiatives, execute complex projects end-to-end, and present actionable insights to executive leadership to mitigate fraud.

Lead credit-risk analytics for consumers and small businesses using large payment/customer data sets. Build attributes and models, deliver executive-ready insights, monitor emerging risks, prioritize high-impact projects, and identify GenAI opportunities to modernize analytics.

Lead end-to-end data science for lending products: define measurement frameworks, analyze experiments and cohorts, evaluate risk and model performance, use AI tools to accelerate analysis, build reusable tooling, and mentor junior scientists while partnering with product, engineering, and risk stakeholders.

Lead end-to-end data science for lending products: define measurement frameworks, analyze customer and risk data, design experiments, monitor model and policy performance, and build reusable tooling. Use AI tools and agent workflows to accelerate analysis, mentor junior scientists, set technical standards, and partner with product, engineering, and risk to drive product and business decisions.

Lead end-to-end data science for lending products: define metrics, run experiments, build measurement systems, evaluate model and policy performance, use AI tools to accelerate analysis, and mentor the team while partnering with product, engineering, and risk stakeholders to drive credit product decisions.

Partner with Marketing to own attribution, ROI, conversion, and funnel analysis. Design experiments, surface proactive insights, optimize marketing spend, and collaborate with Data/Analytics Engineers to build data models and pipelines. Communicate findings and recommendations to non-technical stakeholders.

Lead analytics initiatives to deliver strategic insights for consumer markets. Develop advanced analytics and BI solutions, manage teams, engage stakeholders, implement cloud analytics, and coach diverse teams to improve performance and decision-making.

The Data Scientist will analyze data to provide insights, partner with teams for metrics, develop models, and create accessible visualizations to influence product and program strategies.

Partner with Digital Assets, Finance, Treasury, and Risk to design measurement frameworks, perform strategic analysis and modeling, build scalable SQL-driven automation and dashboards, analyze on-chain and finance data, create visualizations, and drive data-informed product decisions for USDC and related digital asset offerings.