Data Science Lead

Elliptic is the leader in digital asset decisioning. This role leads data science inside Elliptic's Intelligence team, alongside Intelligence Collection, Research and Investigations, and Professional Services. It is a player-coach role with a prospect of scaling a team. At first the role leads a small team of data scientists, owns the team's direction, performance and growth, and owns the evidence for the models the team builds, representing them at Elliptic's model risk governance forum.

Responsibilities

Lead and grow the data science team, setting objectives, owning performance and development, and leveling the team against the Intelligence career framework; Hire the next data scientists, owning the hiring process end to end; Build the collection for the future of compliance, from directive to specification to delivered data; Own model governance for the team's models, including inventory, tiering, validation, monitoring and remediation; Lead Elliptic's data science work by framing research questions and running first experiments; Stay technically hands on by writing code and reviewing the team's method; Own the team's existing commitments on clustering, attribution, heuristics and labelling; Work across functions with Intelligence Collection, Collection Engineering, Research and Investigations, the Chief Scientist, Product and Engineering; Set the AI working standard for the team

Requirements

Demonstrated experience managing data scientists or machine learning engineers, including setting objectives, owning performance including underperformance, and developing people; Experience hiring into a technical team, including defining the bar; Deep, hands on data science and machine learning capability that is current; Strong Python and SQL, with the ability to interrogate large behavioural or transactional datasets and defend method under challenge; AI fluency, demonstrated through applying AI tools within a data science workflow and critically evaluating their output; A track record of taking an ambiguous question to a delivered dataset, model or capability, including deciding what was good enough; Experience with a modern data stack comparable to a cloud data lake, Spark or Databricks, and AWS; Clear communication with technical and non technical stakeholders; Based in Washington, D.C. or willing to relocate there; US citizenship

Nice to have

Work on agentic systems, LLM agents or autonomous transaction flows; Experience building a large dataset or collection from nothing, including schema decisions, quality checks and maintenance; Blockchain or on chain data experience such as clustering, attribution, heuristics, anomaly detection, mempool analysis or privacy preserving systems research; Experience in payments, fraud or risk data science; Having grown a team from a few people to a full department; A PhD or equivalent research training, or a record of published applied research; Experience in a regulated environment or with model risk management frameworks

Benefits

Hybrid working with the option to work from almost anywhere for up to 90 days per year; $650 remote work budget for home office setup; $1,000 annual Learning and Development budget; 25 days annual leave plus 8 US Public Holidays; Extra birthday leave day; Enhanced parental leave of 16 weeks fully paid; Comprehensive medical, dental and vision coverage with generous premium contributions; 401k with company match; Full access to Spill mental health support