DriveWealth
Financial Risk Lead
$200K–$215K
Office - Chicago · Full-time · Posted Jul 31, 2026
- Experience
- 10+ years in financial risk management.
- Visa sponsorship
- Applicants must be authorized to work in the U.S. without current or future visa sponsorship; DriveWealth does not sponsor or support employment authorization, including transfers.
About the job
Leads second-line oversight of financial risks, building firm-wide risk, margin, liquidity, credit, counterparty, and market frameworks while developing the team and applying AI to risk operations.
What you'll do
- Build the financial risk appetite, limits, policies, governance, and reporting for executive leadership and the Board.
- Own margin risk, and oversee liquidity, capital, market access, counterparty, credit, and digital-asset risks.
- Hire and develop the risk team; use agentic AI for counterparty monitoring, exposure aggregation, limit detection, and scenario construction.
What they ask for
- 10+ years in financial risk management, including substantial experience in a broker-dealer, clearing firm, prime broker, or comparable regulated environment and prior risk-program ownership.
- Experience across market, credit, liquidity, and counterparty risk; demonstrated ownership of margin policy, stress testing, and scenario design.
- Working knowledge of clearing and settlement mechanics, including NSCC and DTCC exposure and clearing fund dynamics.
- Practical agentic AI experience; quantitative capability using Python or SQL; and understanding of relevant SEC, FINRA, CFTC, and NFA requirements.
Nice to have
- Exposure to digital assets, stablecoins, or tokenized securities; event contracts, derivatives, prediction markets, or global multi-currency funding.
- Treasury or balance-sheet experience, risk analytics or BI tooling, relevant certifications, Series 7, 24, 27, or 4, or an advanced quantitative degree.
Pay
$200,000 – $215,000 USD (base salary)
Tools
Python, SQL, NSCC, DTCC
AI focus
Use agentic AI for continuous counterparty monitoring, automated exposure aggregation and limit-breach detection, and faster scenario construction.
Model-written summary · the original posting is authoritative