Staff ML Risk Analytics

About the role

This role focuses on combating fraud through advanced machine learning techniques. The successful candidate will be responsible for defining the strategy around machine learning data and features, and for building the entire feature engineering pipeline. This involves identifying any missing tools or infrastructure needed to achieve robust fraud detection.

A key aspect of this position is collaboration with Machine Learning Engineers to ensure that analytical insights are successfully integrated into production-ready ML systems. The role also requires close partnership with Product Managers and Risk analysts to align ML initiatives with broader business goals. This person will act as a central resource for knowledge on the evolving machine learning industry.

Success in this role means establishing the technical direction for the ML Analytics team and mentoring junior colleagues. It requires a deep understanding of machine learning's evolution and a proactive approach to enhancing fraud prevention. The ideal candidate will have a strong background in risk or payments machine learning and a commitment to fighting financial abuse.

The base salary for this position ranges from $98,000 to $162,000, with total compensation potentially including equity, bonus eligibility, and benefits.

Skills that matter here

• machine learning: This role applies machine learning to detect and prevent fraud, requiring the definition of data strategies and engineering pipelines for ML models.
• data science: The position involves analytical work to translate insights into production-ready systems and diagnose infrastructure gaps.
• Spark: Expertise in Spark is required for developing and managing big data machine learning solutions.
• Python: Proficiency in Python is essential for implementing machine learning models and engineering features.
• big data: The role involves working with large datasets to build and optimize machine learning models for fraud detection.
• SQL: SQL skills are necessary for data manipulation and analysis within the machine learning context.

Who this role suits

• You have a minimum of eight years of hands-on experience in machine learning analytics.
• You possess a holistic understanding of how the machine learning industry is developing.
• You are passionate about combating fraud and abuse through technical solutions.
• You are comfortable mentoring others and setting technical direction for a team.

From the employer

What you’ll be doing:

• Define the ML data and feature strategy for fraud detection.
• Own the end-to-end feature engineering pipeline.
• Diagnose gaps between current tooling infrastructure and needed solutions.
• Partner with Machine Learning Engineers to translate insights into production-ready ML systems.
• Set technical direction for the ML Analytics function and mentor junior team members.
• Partner cross-functionally with Product Managers and Risk analysts.
• Serve as the team's institutional knowledge resource on ML industry evolution.

What we look for in you:

• 8+ years of hands-on experience in machine learning analytics or related fields.
• Deep expertise in Spark, Python, and big data ML.
• Proven experience in feature engineering for ML models.
• Holistic understanding of ML industry evolution.
• Background in risk or payments ML is strongly preferred.
• A passion for fighting fraud and abuse.
• Ability to responsibly use generative AI tools in workflows.

What we offer:

• Base salary range: $98K - $162K.
• Total compensation may include equity and bonus eligibility, and benefits (medical, dental, vision, 401(k)).
• Equal Opportunity Employer commitment.
• Reasonable accommodations for individuals with disabilities.