Data Scientist — Blockchain Intelligence

⚡️ About Merkle Science

Merkle Science provides blockchain transaction monitoring and intelligence solutions for web3 companies, digital asset service providers, financial institutions, law enforcement and government agencies to detect, investigate, and prevent illicit use of cryptocurrencies. Our vision is to make cryptocurrencies safe and provide infrastructure for the safe and compliant growth of cryptocurrencies.

Merkle Science is headquartered in New York with offices in Singapore, Bangalore and London. The team has combined experience across Bank of America, Paypal, Luno, Thomson Reuters and Amazon. The company has raised over $27M from SIG, Beco, Republic, DCG, Kenetic, GGV and several others.

About the role

We turn raw on-chain activity into trustworthy intelligence — clustering addresses into real-world entities, attributing them to services and actors, and surfacing risk for compliance and investigations teams. We're looking for a data scientist who is as comfortable shipping a heuristic to production as they are designing it: someone who can move from a messy hypothesis to a working pipeline without waiting on someone else to wire up the data.

You'll work closely with our attribution and clustering leads on models and heuristics that run across billions of transactions and multiple chains (Bitcoin, Ethereum, Tron, Solana, and more).

What you'll do

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.

What we're looking for

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.