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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.

The role:

The Risk Data Science team is looking for a Staff Data Scientist to develop advanced machine learning models, guide measurement, strategy, and data-driven decision making to support various credit risk areas at SoFi. The Staff Data Scientist will work closely with Credit Risk, Product, Engineering teams to design solutions for credit underwriting. These tasks involve researching and applying state of the art machine learning modeling methodologies to solve complex business problems. This role is very rewarding as your work will have a direct and immediate impact on the business’ profitability.

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.

What you’ll o:

The Staff Data Scientist will help SoFi develop better data driven model solutions by:

Proactively identify opportunities and collaborate cross functionally to develop, implement, and continuously improve machine learning models and strategies that support credit underwriting
Proactively identify opportunities to apply AI and advanced machine learning approaches to solve complex business problems
Evaluate alternative data sources and external vendor solutions by defining and driving proof of concept projects to demonstrate the solutions’ business values
Explore and leverage in-house, external, and other open-source machine learning software/algorithms
Contribute in enhancing SoFi’s risk model development code base by developing customized Python scripts or packages
Collaborate with Model Risk Management team and Fair Lending team to demonstrate models are developed with high level rigor that satisfy Model Risk Management requirements, Fair Lending requirements, and other regulatory requirements
Spearhead model deployment by collaborating with cross functional teams including Credit, Product, Engineering, and Business Unit
Present model performance and insights to Credit, Risk, and Business Unit leaders

What you’ll need:

6+ years of work experience in the related areas especially unsecured loan underwriting with a Master’s or PhD degree in Statistics, Mathematics, Economics, Engineering, Computer Science, or a quantitative field.
Excellent knowledge and experience of machine learning and statistical modeling methods for supervised and unsupervised learning. These methods include (but not limited to) regression, clustering, outlier detection, novelty detection, decision trees, nearest neighbors, support vector machines, ensemble methods and boosting, neural networks, deep learning and its various applications. Continuously follow the advancement of machine learning and artificial intelligence to update your knowledge and skills in order to solve business problems with the most efficient methodologies
Strong knowledge of AI and AI safety
Strong programming skills in Python
Strong knowledge of databases and related languages/tools such as SQL, NoSQL, Hive, etc.
Strong presentation skills: able to use data to tell a clear and compelling story; comfortable with public speaking across various forums, including regulatory examinations, and able to effectively and logically communicate when information is being challenged in an open forum.
Ability and confidence to exercise influence over a wide range of individuals at different levels of technical and business leadership.
Someone who is highly motivated and drives change, is eager to learn and able to work collaboratively in a complex and fluid environment

Nice to have:

Experience in communicating with OCC, Fed, and CFPB

Compensation And Benefits

The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate’s experience, skills, and location.

To view all of our comprehensive and competitive benefits, visit our Benefits at SoFi page!

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.

The Company hires the best qualified candidate for the job, without regard to protected characteristics.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

New York applicants: Notice of Employee Rights

SoFi is committed to embracing diversity. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email accommodations@sofi.com.

Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time.

Internal Employees

If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.

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