Senior Financial Risk Analyst

Robinhood Robinhood · Fintech · Chicago, IL +2 · Treasury

This role focuses on financial risk analysis within the fintech domain, involving the development and maintenance of financial risk models and forecasting frameworks. It requires strong quantitative skills, SQL, and Python, with a focus on monitoring and analyzing liquidity risks and partnering with data science and engineering teams for dashboards and alerts. While AI/ML tooling is a bonus, the core responsibilities are in traditional financial risk management and data analysis.

What you'd actually do

  1. Monitor and report on financial risks across the firm on a daily basis, ensuring timely escalation of material exposures
  2. Conduct deep-dive quantitative analyses into liquidity risks emerging across the Robinhood ecosystem
  3. Enhance and maintain liquidity risk models and forecasting frameworks to support proactive risk management
  4. 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
  5. Collaborate with Finance and Product teams to assess emerging risks associated with new products and business initiatives

Skills

Required

  • 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
  • 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

Nice to have

  • Advanced graduate degree such as an MFE or in a similar technical field
  • 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

What the JD emphasized

  • end to end execution and accountability
  • build and own process from the ground up
  • design and own end-to-end processes in ambiguous, fast-moving environments