Data Scientist - Risk
Who We Are
SoFi is a digital personal finance company whose mission is to help its members achieve financial independence to realize their ambitions, whether that be to buy a house one day, start a family on their own terms or be debt free. We aim to be at the center of our members’ financial lives, and to help every member Get Their Money Right®. By joining SoFi, you’ll become part of a forward-thinking company that is transforming financial services by embracing technology to build innovative loan products, investment tools, and more. One of the fastest growing fintech companies, we’ve grown from 250 employees in 2015 to over 1,500 employees today and are well on our way to reaching 1 million members. With offices across the US, we offer the excitement of a rapidly growing startup with the stability of a seasoned management team and some of the best talent around. As an employer, we strive to hire employees who are committed to both our company’s mission and our desire to build the best culture in the world. If you are driven, passionate about what you do, and excited about the SoFi mission, we would love to hear from you.
The Risk Data Science team is looking for a Data Scientist to guide measurement, strategy, and data-driven decision making for the Fraud Prevention team at SoFi. The Data Scientist will work closely with Risk, Engineering, and Operations teams to design solutions to prevent fraudulent behavior, ranging from complex business rules to advanced machine learning models. This role is very rewarding as your work will have a direct and immediate impact on the business’ profitability.
What You’ll Do
Develop, implement, and continuously improve fraud models and strategies
Leverage in-house, external, and other open-source machine learning software/algorithms
Research and apply enhanced data and methodologies to existing models
Write specs and work with the broader Engineering teams for model deployment
Perform ongoing monitoring of the models through the construction of dashboards and KPI tracking
Present model performance and insights to Risk and Business Unit leaders
What You’ll Need
Bachelor’s degree in Computer Science, Statistics, Physics, Engineering, or quantitative field required. Master’s degree preferred.
2-3 years of relevant work experience with building risk models
Excellent knowledge of machine learning and statistical modeling methods for supervised and unsupervised learning. These methods include (but not limited to) regression analysis, clustering, outlier detection, novelty detection, decision trees, nearest neighbors, support vector machines, ensemble methods and boosting, neural networks, and deep learning.
Strong programming skills in Python
Strong knowledge of databases and related languages/tools such as SQL, NoSQL, Hive, etc.
Effective communication skills and ability to explain complex models in simple terms
Nice To Have
Experience in a financial organization
Experience in building fraud detection models
Experience with model documentation
Why You’ll Love Working Here
Competitive salary packages and bonuses
Comprehensive medical, dental, vision and life insurance benefits
Generous vacation and holidays
Paid parental leave for eligible employees
401(k) and education on retirement planning
Tuition reimbursement on approved programs
Monthly contribution up to $200 to help you pay off your student loans
Great health & well-being benefits including telehealth parental support, subsidized gym program
Employer paid lunch program (except for remote employees)
Fully stocked kitchen (snacks and drinks)
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.