SoFi
Financial Services
StaffDataScientist
Neural analysis suggests this role is
optimal for Senior candidates.
“Staff Data Scientist at SoFi. Skills: Model development, performance monitoring, loss forecasting, quantitative and machine learning models, data driven modeling solutions, credit risk modeling. Model development and performance monitoring supporting data driven decision making. Develop loss forecasting across various SoFi products including but not limited to Personal Loans, Student Loans and Credit Cards”
Industry & Context.
Part-time telecommuting is an option, Hybrid work from Sofi offices in San Francisco, CA
What They're Looking For.
Must Have
Master's degree in Statistics, Data Science or related quantitative discipline, two (2) years in the job offered or in any occupation in related field, data analytics and modeling using Python, machine learning econometrics modeling, survival modeling, state transition, Markov, linear regression, logistic regression, decision trees, gradient, developing and productionizing models, SQL for large scale data extraction, transformation, Microsoft Office applications, including Excel, PowerPoint, developing and building loss forecasting, credit risk modeling, including probability of default (PD), loss forecasting, and delinquency, experience working with large datasets in cloud-based data environments or modern data
Nice to Have
Tableau
What You'll Do.
Model development and performance monitoring supporting data driven decision making
Develop loss forecasting across various SoFi products including but not limited to Personal Loans
Student Loans and Credit Cards
Use empirical measurements
develop quantitative and machine learning models to forecast losses and provide insights on the drivers for losses
Develop better data driven modeling solutions
Aggregate and synthesize datasets from multiple data environments
Analyze complex datasets to understand the performance and drivers for losses across various products
Investigate external credit data to identify trends in the market and industry
How You'll Work.
Team & Collaboration
Collaborate with the Business Unit, Finance, Accounting, Credit Fraud Risk groups
Full Job Description
Employee Applicant Privacy Notice Who we are: Shape a brighter financial future with us. Together with our members, we’re changing the way people think about and interact with personal finance. We’re a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront. We’re proud to come to work every day knowing that what we do has a direct impact on people’s lives, with our core values guiding us every step of the way. Join us to invest in yourself, your career, and the financial world. Social Finance, LLC seeks Staff Data Scientist in San Francisco, CA: Job Duties: Model development and performance monitoring supporting data driven decision making. Develop loss forecasting across various SoFi products including but not limited to Personal Loans, Student Loans and Credit Cards. Use empirical measurements, develop quantitative and machine learning models to forecast losses and provide insights on the drivers for losses. Collaborate with the Business Unit, Finance, Accounting, Credit Fraud Risk groups. Develop better data driven modeling solutions. Aggregate and synthesize datasets from multiple data environments. Analyze complex datasets to understand the performance and drivers for losses across various products. Investigate external credit data to identify trends in the market and industry. Part-time telecommuting is an option. Hybrid work from Sofi offices in San Francisco, CA. Requirements: Master’s degree in Statistics, Data Science or related quantitative discipline and two (2) years in the job offered or in any occupation in related field. Special Skill Requirements: (1.) data analytics and modeling using Python and machine learning econometrics modeling, survival modeling, state transition, and Markov Chain; (2.) linear regression, logistic regression, decision trees, and gradient b
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