Staff Data Scientist, SoFi

• 6+ years of loss forecasting experience with 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
• Excellent 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, e.g. econometrics modeling, discrete survival modeling, state transition, Markov Chain 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

Desirable

• We are looking for a Staff Data Scientist to join our Risk Analytics Modeling Team within Risk Analytics. This team member’s responsibilities include model development and performance monitoring supporting data-driven decision-making within our second line of defense.
• The Staff Data Scientist will play a key role in developing loss forecasting and CECL models across various SoFi products including but not limited to Personal Loans, Student Loans and Credit Cards.
• The Staff 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 should have hands-on knowledge on common loss forecasting methodologies (e.g. econometrics modeling, survival modeling, state transition, Markov Chain etc.) and excellent 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.)
• 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