Staff Data Scientist

Job description

Chainalysis is inspired by solving the hardest technical challenges and creating products that build trust in cryptocurrencies. We're a global organization who thrive on the challenging work we do and doing it with other exceptionally talented teammates. Our industry changes constantly, and our job is to create user-facing products supported by our best-in-class data, allowing us to adapt to those rapid changes and bring maximal value to our customers. We're looking for a Staff Data Scientist to join our Research and Intelligence organisation in London. You'll work across flexible, cross-functional squads within the Data Science team, leading analytical, statistical, and machine-learning work across both UTXO and EVM blockchains. The team develops behavioural heuristics, graph algorithms, statistical models, and machine-learning techniques that power some of the most advanced blockchain analysis in the industry. Much of the work is state of the art and ahead of academia; your work will shape the data that fuels Chainalysis products and customers globally. This role is ideal for someone energised by deeply technical, ambiguous problems at the intersection of statistics, machine learning, algorithms, and large-scale on-chain analysis—and who wants to own multiple projects and systems end to end, shape technical direction, and deliver company-level impact. In this role, you’ll: Own and prioritise multiple concurrent production data-science projects or systems, translating broad problem statements into actionable work, managing evolving requirements, and delivering measurable outcomes. Design, develop, and validate novel analytical methods, statistical models, behavioural heuristics, and algorithms across UTXO, EVM, and other blockchain data to attribute on-chain activity and uncover customer-relevant insights. Stay current on advances in data science and blockchain analysis; evaluate and pilot techniques such as graph computation, statistical modelling, and machine learning on large-scale on-chain datasets. Identify and resolve inefficiencies in code, methodology, and workflows; make architectural decisions; define and track quality metrics; understand upstream and downstream dependencies; and balance long-term system health and technical debt against new delivery. Drive cross-functional alignment by clearly articulating and defending methodology and results,…