Data Scientist — Blockchain Intelligence

Our 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 — our lakehouse and processing backbone. Large-scale on-chain datasets are transformed and modeled here 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, so a lot of our work is designed to run incrementally rather than as one-off batch jobs.

• Python — the primary language for everything from exploratory analysis to production heuristics and pipeline code.

• TigerGraph — our graph database, where addresses, transactions, and entities live as a network. Clustering, traversals, and relationship queries (who funds whom, consolidation paths, entity linkage) happen here.

Supporting cast you'll likely touch:

• SQL everywhere — 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 — we expect pipelines and heuristics to be reviewed like any other code.

• GCP as our 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.