About the team + role

We are looking for an intellectually curious and enthusiastic individual to help the organization prepare for continued rapid growth by having a good understanding of the impact of the financial markets and risks on Robinhood. This role is responsible for monitoring corporate liquidity risks and investments, developing financial risk models, forecasting key balance sheet items and providing deep dive analysis into financial market drivers of the business.

This role suits those with a bias towards action and impact, who believe in end to end execution and accountability. You are the perfect mix of analytical, technical, operational, and creative. You are a clear, confident communicator who loves taking a complicated problem, decomposing it, and building data driven models, then turning those models into processes and reports. 

What you’ll do

• Monitor and report on financial risks across the firm on a daily basis, ensuring timely escalation of material exposures
• Conduct deep-dive quantitative analyses into liquidity risks emerging across the Robinhood ecosystem   
• Enhance and maintain liquidity risk models and forecasting frameworks to support proactive risk management
• Partner with Data Science and Engineering teams to design and deliver dashboards, automated alerts, and data pipelines that surface capital, liquidity, and counterparty credit risks for the Treasury function
• Collaborate with Finance and Product teams to assess emerging risks associated with new products and business initiatives

What you bring

• Bachelor's degree in mathematics, computer science, engineering or a related field.
• 1–3 years of experience in data analytics, risk management, or a comparable analytical role
• Strong quantitative and problem-solving skills, with the ability to communicate complex findings clearly to non-technical stakeholder
• Solid grounding in financial risk methodologies including Value-at-Risk, stress testing, scenario analysis, and attribution modeling
• Understanding of macroeconomics, financial markets, financial instruments, trading strategies, and investment theory
• Advanced proficiency in SQL and Python
• Ability to build and own process from the ground up
• Demonstrated ability to design and own end-to-end processes in ambiguous, fast-moving environments

Bonus points:

• Advanced graduate degree such as an MFE or in a similar technical field preferred.
• Experience developing and deploying production-grade financial models in Python or R
• Familiarity with sophisticated financial instruments including derivatives, futures, leveraged products, and digital assets
• Experience with time series modeling and regression techniques
• Hands-on experience with AI/ML tooling and business intelligence platforms such as Superset or Looker
• Working knowledge of ETL pipelines and workflow orchestration tools such as Airflow