Responsibilities
- Design, test, and ship clustering and attribution heuristics, and measure them with real precision/coverage metrics rather than vibes.
- Own your data end to end — pull, clean, join, and model large on-chain datasets without depending on a separate team for every query.
- Build and maintain the pipelines that take a heuristic from notebook to production, including backfills, incremental runs, and validation.
- Investigate edge cases (mixers, bridges, exchange hot wallets, consolidation patterns) and translate findings into repeatable logic.
- Partner with investigations and product to define what "correct" looks like and benchmark against ground truth.
- Prototype quickly, then harden what works.
Requirements
- 4+ years building data science or data engineering systems that actually shipped (not just notebooks)
- Strong Python and SQL; comfortable with large datasets and the gotchas of joins, dedup, and skew at scale
- Solid grasp of clustering, graph/network analysis, or entity resolution — and a habit of validating results, not just producing them
- Ability to reason about precision vs. coverage trade-offs and defend your metrics
- Self-directed: you can scope an ambiguous problem, get the data yourself, and drive it to a result
Work Arrangement
Remote (Worldwide) — New York, Singapore, Bangalore, London
Additional Information
- flexible time off
- learning & development initiatives
- hours that are designed to provide work/life balance
- team-building sessions
- discussions around mental health
- industry-leading compensation
- generous equity
