Lead Quant, Crypto Intelligence Platform

AP Executive are working with a client who are building institutional-grade crypto intelligence terminal. They're creating a unified platform that transforms blockchain data into actionable insights y're building the data layer and infrastructure that makes deep on-chain analysis accessible without specialised expertise.

We're looking for a quantitative researcher to design the analytical frameworks that power our platform. This isn't about implementing someone else's models. This is about defining what "smart money" means, creating signals, and building metrics that don't exist yet.

• How do you score wallet behavior in a pseudonymous environment?
• What signals distinguish accumulation from manipulation?
• How do you measure capital flows across fragmented liquidity?
• What blockchain-native metrics predict protocol success?

You’ll work closely with our Senior Blockchain Data Engineer to turn research into production systems. This is a deeply collaborative role - you’ll need to understand what’s feasible with blockchain data infrastructure and help shape how we architect our data layer to support sophisticated analysis.

• Design quantitative frameworks for analysing onchain behavior (wallet scoring, entity classification, flow analysis)
• Create market microstructure indicators that work across CEXs, DEXs, and L2s
• Build risk and anomaly detection systems for 300M+ wallets
• Develop blockchain-native metrics (realized cap, holder behavior, network effects)
• Research signal validity and backtest against historical data
• Collaborate with data engineering to architect systems that can compute your models at scale
• Turn research into production systems that run automatically

This role is part research, part engineering, part product. You’ll have significant autonomy over what you build.

• You understand blockchain data from first principles, what it measures and what it doesn’t
• You can design metrics that work across different chain architectures
• You think about supply dynamics, holder behaviour, and flow analysis
• You care about creating interpretable, actionable metrics, not just correlations
• You want your research to define how people understand on‑chain activity
• You have strong quantitative foundations (statistics, probability, modelling)
• You've built quantitative models that were used in production
• You're comfortable working with messy, incomplete data
• You can collaborate effectively with engineers and other members to ship your work
• You can explain complex ideas clearly to non-technical stakeholders

• PhD or Master's degree in Mathematics, Statistics, Physics, Computer Science, Economics, Financial Engineering, or related quantitative field
• Preferred :
  Oxford, Cambridge, MIT, Stanford, Harvard, Princeton, Yale, ETH Zurich, Imperial College London.
• Exceptional undergraduate candidates from these institutions with demonstrated research excellence will be considered

• Minimum 3-5 years building and deploying quantitative models in production environments
• Proven track record of models that generated measurable impact (P&L, predictive accuracy, operational improvements)
• Experience with large-scale data analysis and statistical modelling
• Demonstrated ability to take research from conception through production deployment

• Expert-level proficiency in Python or relevant coding language
• Strong foundation in probability theory, statistical inference, and time series analysis
• Experience with back testing frameworks and validation methodologies
• Comfortable writing production-quality code, not just research scripts
• SQL and data manipulation at scale

• Deep understanding of market microstructure and trading dynamics
• Experience analysing financial time series and behavioural patterns
• Familiarity with quantitative finance concepts (risk metrics, portfolio theory, signal processing)
• Blockchain / crypto experience is valuable but not required if you have strong foundations and can learn quickly

We're looking for researchers who have…