Senior Data Scientist, SoFi

• 5+ years of loss forecasting experience with a Master’s or PhD degree
• Proficient in Python, SQL & Tableau
• Experienced in model development and data analysis
• Good understanding in basic Statistical / Machine learning models, e.g. linear regression, logistic regression, survival models, decision trees, random forests, clustering analysis etc
• Strong communications and presentation skills
• Someone who is highly motivated and drives change, is eager to learn and able to work collaboratively in a complex and fluid environment
• Prior experience in credit loss forecasting is a must

Desirable

• We are looking for a Senior Data Scientist to join our Loss Forecasting Team within Risk Analytics
• This team member will work in the team that is responsible for forecasting losses across various SoFi products including but not limited to Personal Loans, Student Loans and Credit Cards
• The Data Scientist will contribute to the performance analysis of SoFi products using empirical measurements, develop quantitative and machine learning models to forecast losses and provide insights on the drivers for losses
• She/He will also collaborate with the Business Unit, Finance, Accounting, Credit & Fraud Risk groups
• This position requires knowledge of data analytics and modeling using Python and machine learning/analytical packages as well as strong problem solving and communication skills
• The ideal candidate will have expertise in analyzing large datasets and developing models using Python, SQL and other statistical analysis packages to get to the truth behind the data
• By joining SoFi, you'll become part of a forward-thinking company that is transforming financial services for the better
• We offer the excitement of a rapidly growing startup with the stability of an industry leading leadership team
• Developing quantitative/machine learning models to forecast product losses
• Aggregating and synthesizing datasets from multiple data environments
• Analyzing complex datasets to understand the performance and drivers for losses across various products
• Investigating external credit 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
• Working with Business Units, Operations, Product, Capital Markets, Finance, Accounting and Risk partners to ensure correct loss expectations and trend of losses are communicated effectively and executed appropriately