SoFi is looking to develop better data-driven modeling solutions to reduce Fraud losses and minimize false positives, ultimately protecting its members.
Requirements
- 5+ years of loss forecasting experience and/or a Master’s or PhD degree in Statistics, Mathematics, Economics, Engineering, Computer Science, or a quantitative field
- Proficient in Python, SQL & Tableau
- Experienced in model development and data analysis with deep knowledge of data science, statistical methodologies and machine learning models, e.g. linear regression, logistic regression, decision trees, gradient boosting, random forests, neural network, clustering analysis etc.
- Hands-on knowledge on common loss forecasting methodologies
- Familiarity working with graph databases
- Experience with developing and productionizing models in the AWS environment
Responsibilities
- Developing quantitative/machine learning models to reduce Fraud losses, and OpEx related to supporting Fraud complaints and disputes
- Aggregating and synthesizing datasets from multiple data environments
- Analyzing complex datasets to understand the performance and drivers for losses across various products
- Investigating external risk data to identify trends in the market and industry
- Conducting loss sensitivity analysis
- Automating models and analytical dashboards
- Monitoring the models’ performance and re-calibrating the models as needed
Other
- 5+ years of loss forecasting experience
- Master’s or PhD degree in Statistics, Mathematics, Economics, Engineering, Computer Science, or a quantitative field
- Highly motivated and drives change, is eager to learn and able to work collaboratively in a complex and fluid environment
- Ability to work in a team environment
- SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, gender, national origin, ancestry, age, physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status
