About the role
Sign3 is an AI-first customer intelligence platform powering fraud prevention, risk scoring, and smarter onboarding decisions for leading FinTechs, banks, and digital businesses. Our flagship SDK and APIs analyze device, behavioral, and digital footprint signals to help clients detect synthetic identities, money mule accounts, and other fraud vectors—in real time. We work with top-tier players across credit cards, lending, gaming, and neo-banking, helping them unlock trusted growth at scale. Our systems process millions of risk events per day, with millisecond-level performance and precision. At Sign3, you’ll get to build in a fast-paced, impact-focused environment alongside a lean, high-ownership team.
The Role
We are looking for a Senior Data Scientist to build and deploy machine learning models that power fraud detection, risk scoring, and credit decisioning for our fintech and banking partners. You will own the end-to-end model lifecycle, from data exploration and feature engineering to deployment, monitoring, and iteration, while working closely with engineering and product teams.
What You'll Do
- Develop machine learning and statistical models for fraud prevention, risk scoring, and credit underwriting
- Analyze large-scale behavioral and transactional datasets to identify patterns, anomalies, and emerging fraud trends
- Design and run experiments to evaluate, compare, and improve model performance
- Partner with engineering to deploy models into production and ensure they are reliable, observable, and performant at scale
- Monitor live models for drift and degradation, and lead retraining and improvement cycles
- Collaborate with product and risk teams to translate business problems into modeling solutions
What We're Looking For
- Bachelor's degree or higher in Computer Science, Statistics, Applied Mathematics, or a related field
- 5+ years of experience in data science, with exposure to FinTech or financial services preferred
- Strong expertise in Python and SQL, and hands-on experience with ML frameworks such as scikitlearn, XGBoost, PyTorch, or TensorFlow
- Solid foundation in classical machine learning, statistical modeling, and experiment design
- Experience taking models from notebook to production, including monitoring and iteration
- Ability to communicate complex findings clearly to technical and non-technical stakeholders
- Competitive compensation and meaningful equity
- High-ownership role in a fast-growing AI startup