Staff Data Scientist at Chainalysis

Role Overview

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.

About Chainalysis

Chainalysis is the blockchain data platform, making it easy to connect the movement of digital assets to real-world services. Powered by deep blockchain data and AI, organizations can investigate illicit activity, manage risk exposure, and develop innovative market solutions built on the industry's most trusted blockchain intelligence.

Key Responsibilities

Own and prioritise multiple concurrent production data-science projects or systems, translating broad problem statements into actionable work.
Design, develop, and validate novel analytical methods, statistical models, behavioural heuristics, and algorithms across UTXO, EVM, and other blockchain data.
Stay current on advances in data science and blockchain analysis; evaluate and pilot techniques such as graph computation, statistical modelling, and machine learning.
Identify and resolve inefficiencies in code, methodology, and workflows; make architectural decisions; define and track quality metrics.
Drive cross-functional alignment by clearly articulating and defending methodology and results.

Requirements & Eligibility

Deep expertise in statistics, machine learning, computer science, physics, mathematics, or another quantitative discipline, demonstrated through advanced industry or research work.
Expert-level proficiency in Python and SQL, with a track record of writing clean, testable, production-quality code.
Demonstrated experience applying advanced analytical techniques (e.g. graph algorithms, ML, statistical inference) to large, messy datasets.
Demonstrated ability to learn unfamiliar technical domains and data models quickly.
A demonstrated ability to own and prioritise multiple concurrent projects or systems, make sound architectural and methodological decisions, and drive cross-functional stakeholders toward delivery.