Data Scientist - Risk

• Collaborate with Risk Business Partners and other stakeholders to develop Key Risk Indicators (KRIs) metrics to measure first-line control performance
• Size opportunities and identify levers to reduce risk or bad activity as measured by KRIs and shape the strategic direction of the Risk organization
• Utilize proxy measurement methodologies to estimate and monitor operational and product risks at Block
• Design and implement alerting systems to detect risk regressions and anomalies
• Create metrics and visualizations that size relative risks, identify priorities, and surface opportunities for action
• Partner with product teams during experimentation, rollout, and post-launch phases to measure potential product risks
• Develop experimentable risk metrics and proxies to streamline the measurement of risk outcomes during product launch experiments
• Implement non-experimental analyses (pre/post, synthetic control, regression discontinuity) to estimate changes when experiments are not possible
• Work with stakeholders to quantify the potential risks of new features and provide data-driven recommendations
• Collaborate with the First Line Risk Monitoring team to analyze, build visualizations, and understand potential risks of externally shared data
• Support managing partner risk through data analysis and monitoring frameworks
• Present findings and recommendations to senior stakeholders and non-technical audiences
• Help stakeholders understand tradeoffs and estimate the impact of different risk mitigation alternatives
• Partner with First Line Risk Monitoring leads to scope analytics projects and deliver independently
• Work cross-functionally with Risk product teams, Compliance, Customer Operations, and other Risk org teams

• Minimum 5+ years of post-graduate industry experience in product data science roles
• Master's degree or PhD preferred in a quantitative discipline, or a BA/BS with significant demonstrable experience of advanced data science
• Bachelor's degree required in Data Science, Statistics, Computer Science, Mathematics, Engineering, or related quantitative field
• Strong proficiency in Python and SQL
• Solid understanding of probability and statistics, including A/B test design and evaluation, standard error calculations and statistical inference, and anomaly detection techniques and methods
• Experience with Python visualization packages (e.g. matplotlib / plotly)
• Experience working with git or version control systems
• Experience leveraging LLMs and engineering prompts to accelerate development or analyze non-quantitative data
• Experience with data warehousing platforms (e.g., Snowflake, BigQuery)

Past experience in Fintech and/or trust & safety is a plus

• Remote work
• medical insurance
• flexible time off
• retirement savings plans
• modern family planning