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Fraud Data Scientist (Ref: 191597)
Fraud Data Scientist (Ref: 191597)Forsyth Barnes • San Francisco, CA, United States
Fraud Data Scientist (Ref : 191597)

Fraud Data Scientist (Ref : 191597)

Forsyth Barnes • San Francisco, CA, United States
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This range is provided by Forsyth Barnes. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$120,000.00 / yr - $160,000.00 / yr

Direct message the job poster from Forsyth Barnes

About the Company

Our client is an early-stage pre-seed RegTech startup focused on KYB / KYC and fraud prevention, backed by top-tier advisors and preparing for their next funding round next year. They are on a mission to transform compliance and fraud detection through innovative data-driven solutions.

The Role

They are looking for a hands-on, highly motivated Data Scientist / Data Engineer to join their founding team. This is a unique opportunity to help build data infrastructure, labeling pipelines, and analytics capabilities from the ground up. You’ll work across the full data stack, shaping the company’s data strategy and solutions.

Key Responsibilities

  • Build and maintain data infrastructure and labeling pipelines.
  • Develop models and analytics for fraud prevention and KYB / KYC processes.
  • Collaborate closely with a small IT team (currently 5 members) and advisors.
  • Own projects end-to-end : design, implement, test, and iterate solutions.
  • Translate complex business problems into actionable data-driven solutions.

Candidate Profile

  • 5+ years of experience in data science, data engineering, or a dual role; exceptional candidates with slightly less experience may be considered.
  • Strong experience in RegTech, financial crime, fraud detection, or compliance systems.
  • Prior experience at a KYC / KYB provider, AML team, or financial institution is highly desirable (e.g., fraud teams at fintechs like PayPal).
  • Hands-on builder who thrives in a fast-moving startup environment.
  • Side projects, personal builds, or contributions to the data community are a strong indicator of fit.
  • Seniority level

    Mid-Senior level

    Employment type

    Full-time

    Job function

    Information Technology

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