Senior Data Scientist, Risk

Block

4h•$168,300 - $297,000•Remote

About The Position

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. Work from anywhere: This role can be performed from any location in the US with the flexibility to work from home

Requirements

8+ years of post-graduate industry experience in product data science; experience in risk is a plus
Deep experience in product A/B testing, including experiment design, analysis, interpretation, and guiding decisions under imperfect data
Track record of influencing product and risk strategy through data-driven insights and strong cross-functional partnerships
Proven ability to build and own metrics, dashboards, and monitoring systems in fast-paced, evolving environments
Ability to communicate complex technical concepts clearly to non-technical audiences and influence without authority
Strong stakeholder management skills, including navigating trade-offs across Product, Engineering, and Risk
Strategic thinker who balances analytical rigor with business pragmatism and long-term impact
Proactive mindset with the ability to identify emerging risks and opportunities through data patterns
Self-directed operator who can independently drive projects end-to-end after collaborative scoping
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
Proficiency in Python (e.g., Pandas, NumPy) and SQL (e.g., Snowflake, BigQuery, MySQL)
Experience with analytics and BI tools (e.g., Looker, Mode)
Familiarity with Git and version control workflows
Strong foundation in probability and statistics, including A/B experiment design and evaluation, statistical inference, and anomaly detection
Experience leveraging LLMs and prompt engineering to accelerate development or analyze non-quantitative data

Responsibilities

Analyze risk actions and quantify impact on losses and seller experience
Evaluate effectiveness and trade-offs of risk controls
Design, execute, and analyze A/B tests to validate features and optimize risk decisions
Apply statistical and modeling techniques to understand customer behavior and inform strategy
Collaborate with engineers to log and curate data for analysis
Build metrics, dashboards, and visualizations to monitor performance and guide decisions
Partner cross-functionally to translate insights into actionable product and risk improvements
Communicate findings and recommendations clearly to team leads and stakeholders