Data Scientist, Blockchain Intelligence

Company: Merkle Science

Location: Remote (Europe)

Employment Type: Full-time

About the Company

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. The vision is to make cryptocurrencies safe and provide infrastructure for safe and compliant growth. 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 others.

About the Role

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. You'll work closely with 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 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.

Tech Stack

You don't need to have used all of these, but here's what you'd be working with day to day:

• Databricks—lakehouse and processing backbone. Large-scale on-chain datasets are transformed and modeled via Spark and SQL; most heuristics run as Databricks jobs against billions of transactions.
• Kafka—real-time ingestion of on-chain and transaction data. New blocks and events stream in continuously.
• Python—primary language for exploratory analysis to production heuristics and pipeline code.
• TigerGraph—graph database where addresses, transactions, and entities live as a network. Clustering, traversals, and relationship queries happen here.
• SQL—for ad-hoc analysis, validation, and defining ground-truth datasets.
• Columnar / analytical stores (e.g., ClickHouse) for fast aggregate queries over large tables.
• Orchestration & scheduling for backfills and recurring pipeline runs.
• Git / GitHub for version control and code review.
• GCP as cloud environment.

How We Work

Small, high-trust team. You'll have a lot of ownership and very little bureaucracy. We prototype fast, measure honestly, and ship.

Well Being, Compensation and Benefits

• Excellent health insurance.
• Flexible time off.
• Learning and development initiatives.
• Hours designed to provide work/life balance.
• Regular team-building sessions.
• Discussions around mental health encouraged.
• Industry-leading compensation.
• Generous equity.