Sr. Data Scientist, Fraud & Platform Integrity

About the company:

We’re partnering with the team behind one of the most widely used fantasy sports and gaming platforms who serve millions of passionate users & is backed by top-tier investors. Their product is mobile-first, socially driven, & scaling fast across new game formats and user communities.

About the Role

We’re hiring a Senior Data Scientist to build & scale fraud detection systems for a high-growth, transaction-heavy consumer platform.

This role sits at the intersection of data science, engineering, and operations. You’ll own the end-to-end development of data pipelines, risk signals, and models that identify fraudulent behavior, strengthen onboarding integrity, as well as enabling faster response from internal teams.

This is not a traditional analyst role; You’ll work hands-on with raw event data, build real-time systems, as well as create tools that empower non-technical teams to detect and mitigate fraud.

What You’ll Do

• Surface fraud/abuse patterns earlier through better data and signal design
• Reduce time to detection and mitigation by enabling internal teams with the right tools
• Build scalable data systems that turn raw events into actionable insights
• Shape onboarding and user flows to minimize risk without adding unnecessary friction
• Continuously improve models and systems as user behavior evolves

Must-Have Qualifications

• 4+ years of experience in data science, machine learning, or applied analytics
• Strong hands-on experience with Python and SQL
• Experience working with event-based data and real-time systems (e.g., Kafka, Spark, Airflow)
• Background in building models (classification, anomaly detection, clustering) and iterating on performance

Nice to Have

• Experience in fraud, risk, trust & safety, or abuse detection
• Experience in high-volume transaction environments (e.g., gaming, fintech, marketplaces)
• Familiarity with tools like Firebase, Amplitude, or Retool
• Experience building internal tools or workflows for operational teams