• Conduct data discovery and exploratory data analysis across structured and unstructured data • Perform data cleansing, normalization, aggregation, and modeling • Design and build analytic applications in Palantir Foundry, including dashboards and interactive workflows • Apply advanced analytics and statistical methods such as regression, clustering, hypothesis testing, and A/B testing • Develop and embed AI/ML capabilities (LLMs, NLP, RAG/GraphRAG) into analytic workflows • Create clear visualizations and metrics to support executive and operational decision‑making • Collaborate in an Agile/Scrum environment to rapidly iterate on solutions • Communicate insights effectively to both technical and non‑technical stakeholders

• Lead data management for complex, global clinical trials from study setup to database lock • Serve as the main client contact for all data management deliverables • Oversee SAE, PK, and external data reconciliation and database deployment • Manage timelines, budgets, and resource allocation to meet customer expectations • Mentor junior data management staff and promote best practices • Drive process improvements and ensure regulatory and SOP compliance

• Work directly with stakeholders (engineers, program managers, subject matter experts) to scope problems, formulate the right analytical questions, and translate business challenges into rigorous data science approaches • Apply deep analytical thinking to decompose complex problems—critically evaluate data quality and relevance, challenge assumptions, and design methods that address the business need • Develop statistical models, machine learning solutions, and analytical frameworks that deliver actionable insights and drive operational decisions • Build user-friendly, production-grade ML/AI applications (e.g., Streamlit, Dash) and analytical artifacts that provide data insights to teams across the enterprise and enable better decision making • Write production-quality Python code and develop analytical pipelines using cloud-based platforms (AWS, Databricks) to support scalable and reproducible data science workflows • Deliver insights through multiple channels—executive recommendations, analytical reports, interactive dashboards, and direct consultation with business leaders • Take ownership of the technical quality and business impact of your work—make thoughtful methodological decisions, validate approaches rigorously, and stand behind your recommendations • Stay current on analytical methods, statistical techniques, and domain-specific best practices to continuously improve the quality and impact of your work

Role Description The Data Science team at Block turns unique customer and product data into decisions that expand access to financial services. Our Lending team powers decisioning behind Cash App Borrow, Afterpay, Square Loans, and the next generation of first-party credit products. We’re looking for a Product Data Scientist to help build, measure, and improve credit products that serve customers traditional credit systems often miss. You’ll partner closely with product, engineering, and risk teams to define metrics, evaluate experiments, understand customer behavior, and turn ambiguous product questions into clear decisions. This is an agentic data science role. You’ll use AI tools and agent workflows to move faster and think more rigorously across the full data science loop: - Exploring messy datasets - Building pipelines - Stress-testing hypotheses - Evaluating product changes - Turning analysis into decisions You Will: - Turn complex product, customer, and risk data into clear insights, decision frameworks, and durable measurement systems for product, risk, and business partners - Use AI tools and agent workflows to improve both the speed and quality of analytical work - Own end-to-end execution across analysis, metrics definition, experimentation, forecasting, visualization, and decision support - Define and maintain measurement frameworks for credit products, including customer eligibility, repayment behavior, product usage, loss performance, funnel health, and long-term customer outcomes - Partner with risk teams to evaluate model and policy performance, monitor cohorts, identify bias or drift, and connect risk decisions to product and business impact - Design and analyze experiments, rollouts, and policy changes that shape customer access, repayment outcomes, and product growth - Approach ambiguous product and risk questions from first principles - Communicate insights clearly to technical, product, and business stakeholders - Lead technical direction and standards for the Block Data Science team - Drive localized cross-team impact by connecting measurement and insights across Lending products - Mentor and grow the team by developing junior data scientists Qualifications - A bachelor’s degree in statistics, data science, economics, computer science, or a similar quantitative field with 12+ years of experience in a relevant role OR - A graduate degree in statistics, data science, economics, computer science, or a similar quantitative field with 6-8+ years of experience in a relevant role - Advanced proficiency with SQL and experience building clear, decision-oriented data visualizations - Strong product, analytical, and statistical judgment - Experience using AI tools to improve the speed, quality, and durability of analytical work Requirements - Ability to turn ambiguous product, customer, or risk questions into sound analyses - Communicate tradeoffs clearly and support decisions Benefits - Remote work - Medical insurance - Flexible time off - Retirement savings plans - Modern family planning