Data Scientist — Blockchain Intelligence

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.

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.

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