Merkle Science - Data Scientist — Blockchain Intelligence
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Requirements
• 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.
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.
Benefits
• We care about your well-being. Along with excellent health insurance, we offer flexible time off, learning & development initiatives and hours that are designed to provide work/life balance. We regularly host team-building sessions and encourage discussions around mental health. • We reward talent and believe in acknowledging people for their contributions. We offer industry-leading compensation, along with generous equity. As a rapidly growing business, there are endless opportunities to grow your career with Merkle Science. • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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