Data Scientist
Transak helps developers integrate fiat-to-crypto payment solutions that allow users to buy and sell crypto directly within web3 applications.
Maintainer signals as of 8/23/2026
Funding history
Projects
About Transak
Transak helps developers onboard users from fiat to crypto through API-driven payment solutions that support transactions in multiple countries. With it, you can integrate fiat on-ramps, enable direct smart contract interactions with fiat, and access payment methods across multiple blockchains. Transak reduces the complexity of crypto onboarding for web3 applications.
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.