Data Science Lead
Elliptic helps businesses detect and prevent financial crime in crypto through blockchain analytics and compliance solutions.
Funding history
Investors
About Elliptic
Elliptic provides blockchain analytics tools for financial institutions, crypto businesses, and regulators to manage risk and investigate crime. Their platform enables real-time wallet screening, transaction monitoring, and cross-chain investigations to ensure regulatory compliance and prevent financial crime.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will lead and grow a data science team while remaining hands-on with code, research, and technical decisions. You will define and deliver datasets and model capabilities, establish model-governance evidence and monitoring, hire and develop data scientists, and partner across functions to deliver reliable platform capabilities.
Requirements
- Demonstrated experience managing data scientists or machine learning engineers, including performance management and people development
- Experience hiring into a technical team and defining the hiring bar
- Deep hands-on data science and machine learning capability
- Strong Python and SQL skills
- Ability to interrogate large behavioural or transactional datasets and defend methodology
- Track record of delivering datasets, models, or capabilities from ambiguous questions
- Experience with a cloud data lake, Spark or Databricks, and AWS
- Clear communication with technical and non-technical stakeholders
- AI fluency, including applying and critically evaluating AI tools in data science workflows
- Based in Washington, D.C. or willing to relocate there
- US citizenship
Responsibilities
- Lead, develop, and manage data scientists, including setting objectives, managing performance, and creating development paths
- Own end-to-end hiring for additional data scientists
- Define, sequence, and deliver data collections for future compliance capabilities
- Maintain model inventory, tiering, validation, testing evidence, monitoring, review submissions, and issue remediation
- Frame research questions, run initial experiments, and recommend data science direction
- Write code, review technical methods, and make key technical decisions
- Own the reliability and trade-offs of clustering, attribution, heuristics, and labelling work
- Partner with collection, research, investigations, product, and engineering functions
- Set and enforce AI usage and verification standards for the team
Benefits
- Hybrid working
- Option to work from almost anywhere for up to 90 days per year
- £500 remote working budget
- $1,000 learning and development budget
- 25 days of annual leave plus bank holidays
- Additional birthday leave day
- 16 weeks of fully paid parental leave for eligible employees
- Private health insurance
- Access to Spill mental health support
- Life assurance coverage of four times salary
- Cycle to Work Scheme
Hiring Process
Dedicated interview stage assessing AI fluency using the candidate's own tooling.