Hands-on DeFi experience: lending protocols, ERC-4626 vaults, AMMs, or oracle systems — as a builder or a sophisticated user
Solidity / EVM literacy: reading protocol contracts, forked-chain simulation (anvil), or writing adapters; non-EVM experience (e.g., Solana) also valued
On-chain operational experience: multisig workflows, transaction submission and signing infrastructure, bridging
TypeScript, and familiarity with a modern data stack (e.g., BigQuery, Dagster, Hex, GCP/Kubernetes)
Experience with risk modeling for volatile or thinly-traded assets: VaR, liquidation modeling, stress testing
Wants end-to-end ownership — research, code, deployment, and the pager — not a hand-off between research and engineering
Comfortable that crypto markets don't close. Strategy owners take on-call seriously, and the occasional market-event night is part of the job; we staff and rotate to keep it sustainable
Operates well in ambiguity: can take "we should have a strategy for X" and return a scoped design, not a list of questions — and is comfortable in a fast-moving space where priorities and team structure evolve
Holds a genuine risk view and voices it — including "we shouldn't do this" when the analysis says so
Pragmatic about shipping: balances rigor against client timelines without cutting corners on safety
Naturally curious about digital assets and DeFi. Deep crypto experience is not required — curiosity and strong quant fundamentals are
A track record of building quantitative systems that run in production — algorithmic trading, portfolio optimization, market making, or risk systems at a trading firm, asset manager, fintech, or crypto-native company. This is often 2–8 years of experience, but we weight what you've built over years on a résumé
You write the code behind your strategies. Strong Python and solid software-engineering fundamentals: testing, code review, and the judgment to build durable abstractions rather than one-off scripts. This is a hands-on, quantitative role — not a discretionary trading seat
Applied quantitative skills: optimization, statistics, and simulation, and the instinct to validate models against real data before trusting them
Comfort with data infrastructure: SQL and experience building or consuming data pipelines
Production ownership: you've debugged live systems under pressure and understand that a strategy managing other people's money has to be correct, monitored, and recoverable
Clear technical communication