Quantitative Risk Analyst

We’re hiring on behalf of a well-backed digital assets business for a Quantitative Risk Analyst to help expand and automate a portfolio allocation framework across DeFi lending markets. This is a hands-on role spanning research, implementation, and live production monitoring.
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This opportunity would suit someone who enjoys working close to live markets, building robust quantitative systems, and taking ownership of models that directly influence capital allocation.
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The Role

Our client is building a risk-first allocation engine for on-chain credit and lending strategies. Rather than relying on simple yield-chasing heuristics, they take a quantitative approach to portfolio construction, using explicit constraints, simulation, and scenario analysis to guide decisions.
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The successful candidate will work closely with senior quantitative leadership to develop and refine allocation models used in live DeFi environments. This is not a purely theoretical position — the work will move from research into production and ongoing operational oversight.
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Key Responsibilities

• Enhance and automate portfolio allocation and rebalancing models across DeFi lending venues
• Support the evolution of optimisation frameworks from traditional portfolio models toward more constrained, risk-aware methodologies
• Incorporate risk metrics such as liquidity assessments, volatility modelling, dependency analysis, and loss forecasting into portfolio decisions
• Develop and improve stress testing frameworks, including Monte Carlo analysis, historical scenario replay, and tail-risk testing
• Build monitoring tools, key risk indicators, automated alerts, and control mechanisms that inform allocation decisions in real time
• Oversee the full lifecycle of model deployment, including testing, production release, performance monitoring, and iteration
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Technical Environment

• Python
• NumPy, Pandas, SciPy
• On-chain data infrastructure including RPC endpoints, indexers, and subgraphs
• Quantitative risk tooling, including Monte Carlo and VaR-style frameworks
• DeFi lending protocol exposure
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Requirements

• Master’s degree or PhD in Mathematics, Physics, Quantitative Finance, or a related quantitative discipline
• Around 5+ years of experience in quantitative risk, portfolio construction, or asset allocation
• Strong background in portfolio construction, optimisation, and risk budgeting within production settings
• Practical experience building and calibrating VaR / CVaR or related downside risk models
• Experience designing reliable backtesting and scenario analysis frameworks
• Familiarity with on-chain data pipelines and blockchain data ingestion
• Working knowledge of EVM-based systems, including lending mechanics, liquidations, oracle design, and borrowing markets
• Ability to read and interpret smart contract logic with confidence
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Desirable Experience

• Previous exposure to DeFi vaults, lending allocators, or credit strategies
• Knowledge of major DeFi lending ecosystems
• Research publications or open-source contributions in quantitative finance, crypto, or risk systems
• Exposure to Rust or Solidity
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Candidate Profile

We’re looking for someone who is highly rigorous and takes production quality seriously. You should be comfortable owning your work end-to-end, from model design through to deployment and monitoring. This role suits an individual who values reproducibility, clear methodology, and robust decision-making under live market conditions.
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The ideal candidate will be self-directed, proactive, and genuinely interested in DeFi as a technical and financial environment. Prior hands-on exposure to on-chain lending or credit markets would be advantageous.
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Working Environment

This is an opportunity to join a lean, high-calibre, distributed team working on technically complex problems in digital assets. The culture is best suited to people who are comfortable with autonomy, accountability, and a high level of ownership.

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