Data Scientist - Risk

Block•San Francisco, CA

About The Position

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. The Product Data Scientist role is embedded within the First Line Risk Monitoring (FLRM) team, enabling the accelerated development of automated risk monitoring capabilities across Block's products. This individual will serve as an analytics and measurement expert, charged with developing key risk indicators (KRIs), risk metrics, and alerting systems to enable proactive risk management. This position will leverage your skills to deliver impact by identifying opportunities to strengthen first line controls, instrument monitoring metrics, and helping to automate detections of emerging risks.

Requirements

Minimum 5+ years of post-graduate industry experience in product data science roles.
Demonstrated experience building metrics, dashboards, and monitoring systems in fast-paced environments.
Track record of partnering with cross-functional teams to deliver analytics that drive decision-making.
Proven ability to communicate complex technical concepts to non-technical stakeholders.
Strong stakeholder management skills with ability to influence without authority.
Excellent communication skills, including translating technical analysis for business audiences.
Collaborative approach to problem-solving with ability to present point of view and facilitate tradeoffs.
Strategic thinking with ability to balance analytical rigor with business pragmatism.
Proactive mindset with ability to identify emerging risks through data patterns.
Self-starter who can independently deliver projects after collaborative scoping.
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.
Comfort writing clean, organized, and testable code and contributing to software applications that implement data science and analytical artifacts in pipelines or front-ends.
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).

Nice To Haves

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

Responsibilities

Collaborate with Risk Business Partners and other stakeholders to develop Key Risk Indicators (KRIs) metrics to measure first-line control performance, identify regressions, and estimate residual risk across Block brands (e.g. Cash App, Square, Afterpay).
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.