Senior Quantitative Researcher

About us

At LlamaRisk, we're making risk analysis in decentralized finance smarter, simpler, and radically more transparent. Our platform helps protocols, investors, and institutions understand and monitor complex financial systems, bridging traditional finance and DeFi with actionable insights and intuitive tools.

Founded in 2021, LlamaRisk is a remote-first company with a globally distributed team of engineers, researchers, and product thinkers. We're focused on building robust, data-driven frameworks that support better decisions in on-chain finance. Above all, we value clarity, security, and innovation grounded in real-world impact.

The Role

We're hiring a Senior Quant Researcher to lead the development of the methodologies that make rigorous, repeatable, and increasingly automated risk parameter management possible across Aave lending market instances. The role sits at the intersection of applied financial modeling, DeFi mechanics, and the implementation of dynamic systems. The Senior Quant Researcher sets the technical bar for the R&D team's output and personally contributes to its most ambitious research.

Two vectors define the work. The first is dynamic risk management models: the risk and econometrics framework that turns parameter management from a manual, judgment-driven process into a defensible, automatable one. The second is market primitives: the next generation of market design, with current focus on Aave v4 architecture and new risk parameter levers, alongside the safe integration of real-world assets onto on-chain lending, including tokenized equities, commodities, and the hedging structures needed to make them workable.

The role carries latitude on how the research agenda is shaped and executed. The methodologies it produces feed parameter decisions on lending markets holding billions in deposits, so a model that is wrong, or right for the wrong reason, carries real downstream cost. The Senior Quant Researcher turns an open risk question into a methodology that is:

• Rigorous. Grounded in sound probability, stochastic modeling, optimization, and statistics, so a parameter choice survives external scrutiny.
• Defensible. Every recommendation traces back to an explicit model and assumption set rather than analyst judgment alone.
• Automatable. The methodology reduces to rules a system can run, with human review reserved for the points that need it.
• Reproducible. The same inputs produce the same output, so any party can rerun the model and reach the same parameter.
• Stress-aware. Asset behavior under stress and the paths to bad debt formation are modeled explicitly, not assumed away.
• Implementable. The methodology fits the data and compute that production can actually surface, and adapts when a constraint binds.

What you'll do

• Design and refine the quantitative methodologies for lending market parameter management, spanning cap setting, interest rate curve calibration, liquidation parameter optimization, stress scenario construction, and the bad debt formation models that sit underneath each of these
• Build toward a framework that can drive automated parameter management with human review at the right intervention points
• Contribute to the design and evaluation of new lending market structures, including Aave v4 architecture (Hubs, Spokes, Caps, Liquidation Engine) and the safe integration of real-world assets: tokenized equities, commodities, and the hedging strategies that make these assets functional as collateral
• Lead a small team of researchers, peer-review output before it ships, unblock researchers at the ideation or process stage, and keep credible track of day-to-day progress
• Work with the Head of Research and engineering counterparts on implementation feasibility, data requirements, and the practical constraints of getting a methodology into production, adapting a model when it is too expensive to run continuously or relies on data that cannot be reliably surfaced
• Improve the quality of the R&D team's research output

Let's connect if you have

• Modeling: Demonstrated experience designing financial or risk models and methodologies, ideally deployed against real exposure
• Mathematics: Strong mathematical and analytical foundations across probability, stochastic processes, optimization, and statistics
• DeFi: Solid working understanding of DeFi protocols, with depth in lending market mechanics, asset behavior under stress, and the operational realities of on-chain markets
• Leadership: Experience leading a small research team or comparable group, including peer review, technical mentorship, and managing a research agenda across multiple people
• Tooling: Fluency in Python and the standard quantitative research stack (Jupyter, pandas, numpy, scipy, modeling and simulation libraries)

Nice to haves

• Knowledge of traditional financial instruments and econometrics or financial mathematics
• Statistical machine learning, especially in settings with limited or non-stationary data
• Reinforcement learning experience
• Control theory, particularly feedback systems and parameter regulation
• Prior research published in financial mathematics, market microstructure, or related fields

What we offer

• Competitive salary paid monthly in USD stablecoin
• A chance to help build critical infrastructure for the future of onchain finance
• Access to cutting-edge technologies and problems in DeFi and real-world assets
• Autonomy, flexibility, and real influence over how our risk methodologies are built
• A lean, ambitious team focused on quality and long-term impact
• Remote-first, globally distributed team