Staff Data Scientist
Chainalysis is a blockchain data and intelligence company providing AI-powered software, data, services, and research for crypto investigations, compliance, fraud prevention, market intelligence, and Web3 security.
Funding history
Projects
About Chainalysis
Chainalysis supplies blockchain intelligence and AI-powered solutions to government agencies, cryptocurrency exchanges, financial institutions, regulators, and cybersecurity companies. Its portfolio spans investigation, transaction monitoring, fraud prevention, risk assessment, threat intelligence, on-chain security, professional services, training, and research.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will lead multiple production data-science projects, turning broad problems into measurable outcomes. You will develop and validate analytical methods, statistical models, behavioural heuristics, and algorithms for large-scale blockchain data. You will make architectural decisions, improve workflows and code quality, communicate methodology and results, mentor colleagues, and help move research prototypes into dependable production systems.
Requirements
- Deep expertise in a quantitative discipline, demonstrated through advanced industry or research work
- Expert-level proficiency in Python and SQL
- Experience applying advanced analytical techniques to large, messy datasets
- Ability to learn unfamiliar technical domains and data models quickly
- Ability to own multiple concurrent projects or systems and drive cross-functional stakeholders toward delivery
- Strong written and verbal communication skills
- Ability to explain complex methodology, mentor technical practitioners, and share knowledge
Responsibilities
- Own and prioritise multiple concurrent production data-science projects or systems
- Design, develop, and validate analytical methods, statistical models, behavioural heuristics, and algorithms for blockchain data
- Evaluate graph computation, statistical modelling, and machine-learning techniques on large-scale on-chain datasets
- Identify and resolve inefficiencies in code, methodology, and workflows
- Make architectural decisions and define and track quality metrics
- Drive cross-functional alignment around methodology, results, and evolving requirements
- Mentor team members, support onboarding, contribute to technical hiring, and share knowledge
- Collaborate to move research prototypes into dependable production systems and build tools or platforms