Integrate and analyze data from credit, identity, and behavioral domains to create new fraud features.
Apply programming and analytical skills for data processing, feature engineering, and model building in cloud environments.
Collaborate with data scientists and engineers to assess the effectiveness of newly developed attributes.
Develop and evaluate predictive models using newly engineered attributes.
Document methodologies and present findings to support knowledge sharing and strategic plans.
Requirements
Currently enrolled in a minimum of a Bachelor's degree program or above in Data Science, Statistics, Computer Science, or related field
Return to school in the Fall of 2026 to complete degree program
Proficient in statistical analysis, predictive modeling, machine learning and working with large datasets
Proficient in Python, PySpark for data analysis and modeling
Familiarity with cloud computing services (AWS)
Knowledge in credit or fraud risk modeling
Additional Instructions
Integrate and analyze data from credit, identity, and behavioral domains to create new fraud features.
Apply programming and analytical skills for data processing, feature engineering, and model building in cloud environments.
Collaborate with data scientists and engineers to assess the effectiveness of newly developed attributes.
Develop and evaluate predictive models using newly engineered attributes.
Document methodologies and present findings to support knowledge sharing and strategic plans.
Perks and Benefits
Fully remote
Volunteer Time Off
Great compensation
Flexible work schedule
Eligible for 401(k) participation in 90 days
