Risk Analytics - Atlas Axais
Job Description
Sky is transforming from a crypto bank into a crypto central bank. We no longer allocate the collateral portfolio directly. We lend to independent allocators, called Prime Agents, who deploy capital across DeFi and RWAs. Your mandate is to help design and iterate the risk engine that keeps Sky safe and protects the USDS peg while letting Primes move fast and win.
This is a builder role for someone who is hungry for outsized impact and upside. Think the New York Fed for a crypto-first, post-Singularity world. If you want to invent robust models, codify them into capital and liquidity policy, pressure‑test them in fast decision forums, and see billions move because of your work … read on.
About Atlas Axis
Atlas Axis serves as Core Council GovOps for Sky, acting as the main governance actor for Sky on a day to day basis. In this role we develop the Atlas, the comprehensive body of rules that define how actors in the Sky Ecosystem interact with each other. One of our main focuses is helping to develop the risk framework that governs all allocators in the ecosystem. As Risk Lead, you will own this workstream and help our team to develop an independent point of view on all risk matters. Your success will be measured not only against the resilience of the Sky risk framework, but also against our team’s ability to contribute substantively as an independent, trusted risk stakeholder.
Where you’ll operate
You'll help build the foundational risk and governance infrastructure that enables Sky to scale from a handful of independent allocators to hundreds.
You'll work at the cutting edge of DeFi, rapidly learning novel crypto primitives with zero historical precedent and translating qualitative unknowns into quantitative risk parameters. You’ll make discretionary decisions that don’t fit within the existing body of rules, especially in the short term. This might include whether an investment in a novel opportunity should be allowed at all, how much capital should be required for a new opportunity, or how Sky level risk considerations should flow down to limits for particular allocators.
Your work will span multiple pillars:
Capital: set portfolio‑level and product‑level capital buffers that scale across asset classes
Liquidity: defend the USDS peg. Define what counts as immediately‑usable vs. quickly‑convertible liquidity and how it supports redemptions and rate policy.
Technical/operational: craft scalable frameworks for smart‑contract, admin, and process risk so novel deployments are enabled safely.
You’ll collaborate in a fast decision cadence with protocol engineers, risk partners, legal, and capital allocators. You’ll navigate high-stakes disagreement in real-time decision forums where allocators and colleagues push back on your assumptions and data.
What you’ll do
Illustrative work you’ll own in your first 90 days
Build operational tooling for our lending markets capital model (Basel‑inspired, adapted to DeFi), including calculators, worked validation examples, and sensitivity analyses.
Introduce interim, liquidity‑aware parameter templates (e.g., exposure tiers with rate add‑ons that scale with market depth), guide them through adoption and develop validation criteria for when to replace them with systematic frameworks.
Propose peg-stability buffers (immediately-liquid vs. convertible assets) with stress scenarios and clear triggers for intervention.
General categories
Build models
Convert qualitative risks (governance privileges, bridge trust assumptions, protocol maturity) into quantitative capital requirements with clear reasoning
Design capital models per asset class: Lending markets; Bond‑like instruments (PTs); Perpetual Strategies; Real‑World Assets (RWAs); and Cash Stablecoins.
Incorporate market, credit, and liquidity risk factors; account for smart‑contract, administrative/governance, and operational risks with practical limits.
Stress test liquidity under adverse scenarios (bank runs, market panics, bridge failures); define gates for scaling liquidity requirements and rate adjustments.
Codify decisions into governance proposals, monitoring dashboards, and automated enforcement where possible.
Monitor live allocations, run post‑mortems. Systematically update models when reality diverges from assumptions; refine parameters as data arrives. Build feedback loops that turn operational experience into better frameworks.