Senior Data Scientist, Fraud Risk Detection

This is a fully remote position, open to applicants in United States.

📋 Description

• Analyze intricate datasets to uncover fraud patterns, attack techniques, and behavioral indicators.

• Collaborate with senior data scientists to convert fraud-related inquiries into testable hypotheses.

• Assist in the development of machine learning models aimed at detecting fraud across account opening, account takeover, and identity risk.

• Assess models utilizing both technical and business metrics such as precision, recall, fraud capture rate, false-positive rate, and customer friction.

• Create and validate features using identity, transactional, behavioral, and other relevant data sources.

• Write clean, well-tested code and collaborate with engineering teams to deploy models and features into production.

• Work alongside the score monitoring team to oversee model and feature performance.

• Provide support for research pertaining to related client inquiries.

• Prepare analyses and effectively communicate findings to both technical and non-technical audiences.

• Collaborate with senior data scientists and engineers from the initial problem definition through feature engineering, experimentation, and deployment.

⛳️ Requirements

• A minimum of 3 years of experience in data science, machine learning, statistical modeling, or a related quantitative field.

• Bachelor's or advanced degree in computer science, statistics, mathematics, economics, engineering, data science, or another quantitative discipline.

• Solid foundation in supervised learning, model evaluation, feature selection, statistical inference, classification, and anomaly detection.

• Proficiency in Python, with the capability to produce clean, readable, and well-tested code.

• Familiarity with libraries such as pandas, NumPy, and scikit-learn.

• Investigative mindset with the ability to derive testable hypotheses from unusual data patterns.

• Familiarity with PySpark, cloud platforms like Amazon Web Services, Google Cloud, Azure, Databricks, and Snowflake, or other large-scale data tools.

• Experience in financial services, FinTech, payments, or another regulated or fraud-intensive sector through coursework, internships, or previous employment.

• Compliance with Experian's data privacy, model documentation, explainability, validation, and governance standards is required.

🏝️ Benefits

• Competitive compensation package along with a bonus plan.

• Core benefits that include medical, dental, vision, and a matching 401K.

• Flexible work environment, with options for remote, hybrid, or in-office work.

• Flexible time off policy, which includes volunteer time off, vacation, sick leave, and 12 paid holidays.

• Comprehensive benefits package.

• Opportunities for variable pay.

• Inclusive and purpose-driven company culture.