Data Scientist
Transak is payments infrastructure for stablecoins and crypto, enabling financial applications to provide fiat-to-crypto and crypto-to-fiat flows, virtual-account payments, compliance, and settlement.
Funding history
Projects
About Transak
Transak provides API- and widget-based crypto payment infrastructure for financial applications. Its offering covers on-ramp, off-ramp, NFT checkout, virtual-account payments, OTC trading, KYC, compliance, payment-method integration, and stablecoin settlement across multiple jurisdictions and blockchain networks.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You'll take genuine end-to-end ownership of how Transak detects and prevents fraud, as part of a small, senior team. Your mandate is to stop fraud while approving as many legitimate transactions as possible through deterministic heuristics, machine learning, AI agents, or other approaches. You will work with large-scale transaction data and risk signals, make data accessible through Snowflake and self-serve tooling, and support product, growth, experimentation, and internal AI initiatives.
Requirements
- Strong mathematical and statistical foundations.
- Self-starter able to identify, scope, and act on important problems independently.
- Fluent in Python and SQL, with the ability to take models from idea to production.
- Intellectually honest about uncertainty and rigorous in evaluating work.
- Genuine interest in crypto, payments, fraud, and related data.
Responsibilities
- Own fraud, chargeback, and transaction-risk models end to end, from framing questions through features, rules, shipping, and thresholds.
- Build and tune machine-learning and signal layers alongside external risk vendors.
- Set the direction of risk modeling and bring the team along.
- Make data accessible through Snowflake, self-serve tooling, dashboards, and internal data assistants.
- Take on product, growth, and experimentation problems.
- Advance internal AI applications, including coding assistants, copilots, and large language model use cases.