Remote ML Engineer - Fraud Detection & Risk Modeling

Harnham; United States; Remote; USD 140,000 - 175,000; Full time

Benefits offered by this job

Annual bonus

Equity/stock options

Unlimited PTO

Strong benefits package

Job summary

Harnham is partnering with a rapidly growing technology company solving fraud and risk challenges at massive scale. This fully remote role lets you own from experimentation to production on large-scale ML systems that influence real-time decisions and protect customers from fraud.

You’ll own the full ML lifecycle, design models for fraud detection, and work with massive datasets in distributed environments.

Qualifications

• 4+ years of hands-on Machine Learning experience in a production environment.
• Strong experience with Python, SQL, and Spark.
• Deep understanding of machine learning, statistics, and experimental design.
• Experience building models from the ground up, not just implementing existing frameworks.
• Fraud, risk, payments, or trust & safety experience is required.
• PhD or Master’s degree in Computer Science, Statistics, Mathematics, or a related quantitative field is highly preferred.

Responsibilities

• Design, develop, and deploy machine learning models focused on fraud detection and risk decisioning
• Own the full ML lifecycle, from experimentation and validation through production deployment
• Work with massive datasets in distributed computing environments
• Develop and improve model evaluation frameworks and performance metrics
• Partner with cross-functional stakeholders to drive measurable business impact

Skills

Python

SQL

Spark

Machine Learning

Statistics

Experimental design

Fraud, risk

Education

PhD or Master’s in CS/Stats/Math

Job description

Harnham is partnering with a rapidly growing technology company solving fraud and risk challenges at massive scale. This fully remote role lets you own from experimentation to production on large-scale ML systems that influence real-time decisions and protect customers from fraud.

You’ll own the full ML lifecycle, design models for fraud detection, and work with massive datasets in distributed environments.