Merkle Science
Posted 74 days agoVerified live 1d ago
Data Scientist — Blockchain Intelligence
Brief overview
Remote
4+ yrsMinimum
PythonSQLDatabricksSparkKafkaTigerGraphGraph databaseClusteringGraph/network analysisEntity resolutionData engineeringData scienceGCPClickHouseGitGitHub
About the company
Merkle Sciencemerklescience.com
Merkle Science is a software company that offers predictive crypto risk and intelligence platforms for businesses and government agencies.
Job description
Summary
Merkle Science provides blockchain transaction monitoring and intelligence solutions for various sectors. They are seeking a Data Scientist to turn raw on-chain activity into trustworthy intelligence, working closely with teams to design and implement clustering and attribution heuristics.
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
Skills
- 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
Qualifications
Must Haves
- 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
Benefits
- Excellent health insurance
- Flexible time off
- Learning & development initiatives
- Hours that are designed to provide work/life balance
- Regularly host team-building sessions
- Encourage discussions around mental health
- Generous equity
- Endless opportunities to grow your career with Merkle Science
