Senior Data Scientist, Risk

<p>Block is one company built from many blocks, all united by the same purpose of economic empowerment. The blocks that form our foundational teams — People, Finance, Counsel, Hardware, Information Security, Platform Infrastructure Engineering, and more — provide support and guidance at the corporate level. They work across business groups and around the globe, spanning time zones and disciplines to develop inclusive People policies, forecast finances, give legal counsel, safeguard systems, nurture new initiatives, and more. Every challenge creates possibilities, and we need different perspectives to see them all. Bring yours to Block.</p> <p><strong>The Role</strong></p> <p>This role will be part of the Risk Product Data Science team, focusing on improving risk outcomes while maintaining a strong seller experience across the Payments Risk domain. The Data Scientist will explore new classes of risk controls, size opportunities, assess impact, and evaluate trade-offs. They will also optimize risk decisioning for payments products, drive product improvements, and enhance the end-to-end seller experience. The role partners closely with Product, Engineering, and Risk teams to deliver data-informed insights, guide strategy, and ensure consistent execution of risk initiatives.</p> <p>*Work from anywhere: This role can be performed from any location in the US with the flexibility to work from home</p> <p><strong>You Will</strong></p> <ul> <li>Analyze risk actions and quantify impact on losses and seller experience</li> <li>Evaluate effectiveness and trade-offs of risk controls</li> <li>Design, execute, and analyze A/B tests to validate features and optimize risk decisions</li> <li>Apply statistical and modeling techniques to understand customer behavior and inform strategy</li> <li>Collaborate with engineers to log and curate data for analysis</li> <li>Build metrics, dashboards, and visualizations to monitor performance and guide decisions</li> <li>Partner cross-functionally to translate insights into actionable product and risk improvements</li> <li>Communicate findings and recommendations clearly to team leads and stakeholders</li> </ul> <p><strong>You Have</strong></p> <ul> <li>8+ years of post-graduate industry experience in product data science; experience in risk is a plus</li> <li>Deep experience in product A/B testing, including experiment design, analysis, interpretation, and guiding decisions under imperfect data</li> <li>Track record of influencing product and risk strategy through data-driven insights and strong cross-functional partnerships</li> <li>Proven ability to build and own metrics, dashboards, and monitoring systems in fast-paced, evolving environments</li> <li>Ability to communicate complex technical concepts clearly to non-technical audiences and influence without authority</li> <li>Strong stakeholder management skills, including navigating trade-offs across Product, Engineering, and Risk</li> <li>Strategic thinker who balances analytical rigor with business pragmatism and long-term impact</li> <li>Proactive mindset with the ability to identify emerging risks and opportunities through data patterns</li> <li>Self-directed operator who can independently drive projects end-to-end after collaborative scoping</li> <li>Bachelor's degree required in Data Science, Statistics, Computer Science, Mathematics, Engineering, or a related quantitative field; Master's or PhD preferred, or equivalent advanced industry experience</li> </ul> <p><strong>Technologies We Use and Teach</strong></p> <ul> <li>Proficiency in Python (e.g., Pandas, NumPy) and SQL (e.g., Snowflake, BigQuery, MySQL)</li> <li>Experience with analytics and BI tools (e.g., Looker, Mode)</li> <li>Familiarity with Git and version control workflows</li> <li>Strong foundation in probability and statistics, including A/B experiment design and evaluation, statistical inference, and anomaly detection</li> <li>Experience leveraging LLMs and prompt engineering to accel ... (truncated, view full listing at source)