Remote Lead ML Scientist - Fraud & Credit Risk

OpenKrill; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Home office stipend

Remote-first culture

Medical, Dental, and Vision Insurance

Job summary

Dave is seeking a Lead Machine Learning Scientist to develop and deploy advanced fraud detection solutions that protect members and responsibly expand credit access. You will own the full ML lifecycle—from data exploration and feature engineering to model deployment, monitoring, and continuous improvement.

You will collaborate with Product, Engineering, and Risk Operations, mentor junior scientists, and push the boundary of fintech ML while maintaining rigor and business impact.

Qualifications

• 7+ years of hands-on ML experience including production deployment.
• Fraud modeling expertise in fintech, credit, or finance.
• Strong Python and SQL proficiency with feature engineering.
• Experience with ML frameworks and cloud-based infrastructure.

Responsibilities

• Build and deploy fraud-detection models identifying synthetic identities and account takeover.
• Explore data, engineer features, evaluate models, and translate findings to production.
• Own end-to-end ML lifecycle from research to deployment and monitoring.
• Collaborate with Engineering, Product, and Risk Operations; mentor teammates.

Skills

ML modeling

Python

SQL

Feature engineering

Production ML systems

Fraud detection

Data analysis

Communication

Education

Bachelor's degree in CS/Math/Stats/Engineering

Tools

TensorFlow

PyTorch

Spark

Cloud ML infra

Job description

Dave is seeking a Lead Machine Learning Scientist to develop and deploy advanced fraud detection solutions that protect members and responsibly expand credit access. You will own the full ML lifecycle—from data exploration and feature engineering to model deployment, monitoring, and continuous improvement.

You will collaborate with Product, Engineering, and Risk Operations, mentor junior scientists, and push the boundary of fintech ML while maintaining rigor and business impact.