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Day 146
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From 11,547 similar postings that came down. Coming down is not always a hire.
Data Scientist — Blockchain Intelligence
- Pay
- Pay not listed
- Work setting
Remote — work setting source
Work setup in the employer’s posting: remote
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-Time
Employment type in the employer’s posting: Full-Time
From the employer’s posting
What you’ll work on
Full postingWe 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 our attribution and clustering leads on models and heuristics that run across billions of transactions and multiple chains (Bitcoin, Ethereum, Tron, Solana, and more).
Design, test and ship clustering and attribution heuristics with precision and coverage metrics
Pull, clean, join and model large on-chain datasets without relying on another team
Build and maintain pipelines for backfills, incremental runs and validation
Investigate edge cases like mixers, bridges and exchange hot wallets
Benchmark results against ground truth with investigations and product teams
From the employer’s posting
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).
Design, test, and ship clustering and attribution heuristics, and measure them with real precision/coverage metrics
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)
Partner with investigations and product to define what "correct" looks like and benchmark against ground truth.
Posting record
Report an errorOpen 4 monthsWe first saw this posting 146 days ago and it is still up. Roles open this long are often slow to fill or kept open to collect applications.
- Status in our records
- Live
- First seen by us
- Jun 19, 2026146 days ago
- Recorded sightings
- 39
- Last seen by us
- Nov 11, 2026
- Employer says posted
- Jun 16, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Merklescience is hiring
5 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- Hiring is picking up
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- ClickHouse
- Databricks
- GCP
- Kafka
Where the posting names them
- 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.
- 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.
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.
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.
- 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
- 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.
Your next step
Complete your application on jobs.lever.co. The employer’s form will show what is required.
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Has Merklescience posted this job before?
Yes. We’ve followed one other Data Scientist — Blockchain Intelligence job there since June 2026. It is still up.
Demand for Data Scientist roles
New Data Scientist postings at the employers we follow are down 4% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
US government figures for data scientists
- Median yearly pay, May 2025
- $120,230
- Expected change, 2025 to 2035
- 34.6% more jobsagainst 3.5% for all jobs
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
Job description
⚡️ 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.
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.
❤️ Well Being, Compensation and 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.
Employment type
Full-Time
Source details
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- Location & working pattern
Belgium
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