Data Science Lead
Elliptic · Washington, D.C.
About this role
**Data Science Lead — Elliptic** Elliptic is the leader in digital asset decisioning, building a comprehensive platform for extracting crypto data and intelligence across blockchains with accuracy and reliability. Founded in 2013 and headquartered in London, Elliptic supports compliance, risk management, intelligence operations, and blockchain infrastructure needs. --- ## Role overview This is a **player-coach** role within Elliptic’s **Intelligence team** (alongside Intelligence Collection, Research & Investigations, and Professional Services). You’ll **lead and grow a data science team** while staying **hands-on with code and method-level technical judgment**. You’ll own the evidence behind your team’s models, represent them at Elliptic’s **model risk governance forum**, and help build the data foundation for how compliance works when the “customer” is an **autonomous agent** rather than a person. --- ## What you will do - **Lead and grow the team**: set objectives, own performance and development, handle difficult conversations, and build real development paths aligned to Elliptic’s Intelligence career framework. - **Hire the next data scientists**: own the hiring process end-to-end (levels, job descriptions, interview panel/bar, and decisions). Team growth to **7–9** over time depends on early success. - **Build the collection for future compliance**: translate directives into specifications and delivered data; define quality bars and be clear about dataset capabilities/limitations. - **Own model governance**: keep model inventory current and correctly tiered; ensure validation/testing evidence exists; monitor drift and act on threshold breaches; submit material changes/deployments with testing artifacts; remediate issues by agreed deadlines. - **Lead data science work**: frame research questions, run initial experiments, and deliver defensible recommendations to Intelligence leadership, the Chief Scientist, and Product. - **Stay technically hands-on**: write code, review at the method level, and make key technical calls yourself. - **Own existing commitments**: ensure reliability and trade-offs for clustering, attribution, heuristics, and labelling work; decide what to stop. - **Work across functions**: partner with Intelligence Collection, Collection Engineering, Research & Investigations, Chief Scientist, Product, and Engineering so outputs become platform capabilities. - **Set the AI working standard**: define how AI is used in engineering/research workflows, what must be verified, and hold the team to it. --- ## What you will achieve in the first 6 months - A **collection plan** at a scale that supports real model/heuristic work, with a documented maintenance path. - An agreed Elliptic position on the evolution of compliance systems, aligned to expected industry demand, with capability work underway or deliberately deferred (with reasons). - At least **one hire** completed, with leveling and development plans in place. - **Model governance in good standing** for everything your team owns (inventory current/tiered, no overdue validations on highest tiers, monitoring in place with acted-upon thresholds, no open issues past remediation deadlines). - Agreed **data science objectives** for Intelligence, with clear expectations for other functions. - Visible improvement in **reliability or throughput** of core team output attributable to changes you made. - Technical credibility maintained: the team trusts your method judgmen
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