Data Scientist, Fraud Risk Detection

Quick Facts

• Role: Data Scientist (Fraud Risk Detection)

Description

You will help investigate new fraud patterns, build features, and train and evaluate machine learning models. You will collaborate with senior data scientists and engineers across the full model development lifecycle—from problem definition and data assembly to feature engineering, experimentation, and deployment. Your work supports fraud detection for account opening, account takeover, and identity risk.

Responsibilities

• Explore large datasets to identify fraud patterns, attack methods, and behavioral signals

• Translate fraud questions into testable hypotheses and analytical approaches

• Build and evaluate ML models for fraud detection across key risk areas

• Evaluate models using technical and business metrics (precision, recall, fraud capture rate, false-positive rate, customer friction)

• Develop and validate features using identity, transactional, and behavioral data sources

• Write clean, well-tested code and partner with engineering to bring models and features into production

• Partner with score monitoring for model/feature monitoring and support research on client questions

• Prepare analyses and communicate findings to both technical and nontechnical audiences

• Apply standards for data privacy, model documentation, explainability, validation, and governance

Requirements

• Bachelor’s degree in a quantitative field (or equivalent experience)

• 1+ years of professional experience in data science, machine learning, statistical modeling, or advanced analytics

• Foundation in supervised learning, model evaluation, feature selection, statistical inference, classification, and anomaly detection

• Proficiency in Python for clean, readable, well-tested code

• Familiarity with pandas, NumPy, and scikit-learn

• Coursework, internship, or project experience related to fraud detection, risk modeling, or adversarial/classification-style problems

• Familiarity with PySpark and at least one cloud/large-scale data platform (AWS, Google Cloud, Azure, Databricks, Snowflake, or similar)

• Exposure to financial services/fintech/payments or another regulated or fraud-intensive industry

Benefits

• Great compensation package and bonus plan

• Core benefits including medical, dental, vision, and matching 401(k)

• Flexible work environment: remote, hybrid, or in-office

• Flexible time off including volunteer time off, vacation, sick time, and 12 paid holidays