Quantitative Risk Modeling Manager

Coinbase Careers Page

17h•Hybrid

About The Position

Ready to be pushed beyond what you think you’re capable of? At Coinbase, our mission is to increase economic freedom in the world. It’s a massive, ambitious opportunity that demands the best of us, every day, as we build the emerging onchain platform — and with it, the future global financial system. To achieve our mission, we’re seeking a very specific candidate. We want someone who is passionate about our mission and who believes in the power of crypto and blockchain technology to update the financial system. We want someone who is eager to leave their mark on the world, who relishes the pressure and privilege of working with high caliber colleagues, and who actively seeks feedback to keep leveling up. We want someone who will run towards, not away from, solving the company’s hardest problems. Our work culture is intense and isn’t for everyone. But if you want to build the future alongside others who excel in their disciplines and expect the same from you, there’s no better place to be. While many roles at Coinbase are remote-first, we are not remote-only. In-person participation is required throughout the year. Team and company-wide offsites are held multiple times annually to foster collaboration, connection, and alignment. Attendance is expected and fully supported. As part of Coinbase’s broader risk management organization, the Risk Quant team is responsible for building quantitative models that assess and mitigate risks associated with our risk-bearing products, as well as measuring and monitoring portfolio exposures across the platform. We are looking for a Senior Quantitative Researcher (IC) to lead the design of Coinbase’s next-generation Global Liquidation Engine. In this role, you will not only manage risk; you will architect the automated machinery that protects the platform during extreme volatility. You will build the algorithms responsible for liquidating distressed portfolios across Spot and Derivatives (Futures, Options) at a global scale. This engine must optimize for aggregated risk reduction, trade slippage, and liquidity fragmentation across multiple venues simultaneously. You will act as the bridge between Financial Risk, Exchange Liquidity, and Engineering, serving as our resident expert on Market Microstructure and Order Book Dynamics.

Requirements

Education: Ph.D. or Master’s degree in a highly quantitative field (Physics, Mathematics, Statistics, Financial Engineering, or Computer Science).
Experience: 6+ years of relevant experience with a Ph.D from a top program.
8+ years of relevant experience with a Master’s degree.
Industry Background: You must have experience in one of the following hybrid areas:
Central Risk Book (CRB) execution trading quant at a Tier 1 Investment Bank.
Default Management risk quant at a major Clearing House (CCP) or Prime Broker.
Electronic Market Making with a specific focus on inventory management, liquidation, or risk constraints.
Technical Expertise:
Deep understanding of Almgren-Chriss frameworks, Order Book Dynamics (L1-L3 data), and Auction Theory.
Experience with Cross-Margining methodologies and models (e.g., offsetting Crypto futures against Spot, or Equity Options against Index futures).
Coding & Engineering:
Production-level proficiency in Python.
Experience deploying quantitative models into production environments and integrating with execution APIs.
Soft Skills: Proven ability to lead cross-functional projects (Product, Eng, Data Science) and communicate complex technical findings to non-technical stakeholders.

Nice To Haves

Hybrid Asset Class Experience: Hands-on experience modeling both Delta-One (Spot/Futures) and Non-Linear (Options/Vol) products.
Data Engineering: Familiarity with modern data pipelines (e.g., Airflow) and real-time data streaming architectures (e.g., Kafka).
Crypto Native: Understanding of specific nuances in crypto market structure (24/7 trading, fragmented liquidity, on-chain vs. off-chain settlement).

Responsibilities

Global Liquidation Engine Design
Design Optimal Liquidation Logic: Develop stochastic control models (adapting Almgren-Chriss frameworks) to determine the optimal trajectory for unwinding distressed portfolios. You must solve for the trade-off between market risk (holding the position too long) and market impact (selling too fast and crashing the price).
Global Optimization: Move beyond single-client liquidation. Build logic that optimizes risk at the aggregated level—managing multiple concurrent liquidations to minimize total market impact and maximize liquidity usage across various venues.
Execution Algorithms & Strategy
Build "Crisis-Ready" Algos: Adapt standard execution benchmarks (VWAP, POV, Implementation Shortfall) for stressed regimes where liquidity evaporates and spreads widen.
Portfolio-Level Unwinds: Develop logic to liquidate based on risk sensitivities (Greeks) rather than just line items (e.g., "Don't just sell the spot asset; buy the put options to flatten the Delta first").
Default Waterfall Architecture
Design and tune the decision logic for the liquidation lifecycle:
Open Market Liquidation (smart order routing and order book interaction).
Internalization / Hedging (Delta-neutralizing the portfolio internally).
Private Auctions (soliciting bids from liquidity providers).
Market Microstructure & Regulation
Liquidity Intelligence: Collaborate with Exchange Liquidity Managers to estimate order market impact and monitor L1-L3 order book dynamics.
Regulatory Defense: Ensure all liquidation logic meets the "Commercially Reasonable" standard. Produce quantitative evidence and backtesting results to justify execution decisions to regulators and institutional clients.