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FinTech, Financial Services

Staff Data Scientist

San Francisco, California, United StatesRemoteFull TimeStaff$153,600–$264,000 /yrPosted 2 months agoVisa sponsorship available

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Role summary

SoFi's Risk Data Science team is seeking a Staff Data Scientist to lead the evolution of their Home Loan and Home Equity risk frameworks. This role involves designing and implementing ML models for mortgage-specific use cases like credit underwriting and property valuation. You will provide technical oversight for externally developed models, ensure compliance with regulatory standards, and partner with Product and Engineering for model operationalization. The ideal candidate has a Master's degree (Ph.D. preferred) in a quantitative field, 5+ years of experience in mortgage lending risk modeling, expert knowledge of ML methods, and strong programming skills in Python and SQL, with hands-on experience in production ML environments.

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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 RoleThe Risk Data Science team is seeking a strategic and technically deep Machine Learning Engineer to lead the evolution of SoFi’s Home Loan and Home Equity risk frameworks. This role is at the center of a high-priority initiative to deliver a comprehensive, loan-level credit risk and valuation framework for our home lending portfolios.

As a key technical leader, you will apply state-of-the-art ML methodologies to solve complex problems—including default and prepayment modeling, property valuation (AVMs), and credit risk assessment—ensuring our high-value secured loan products are backed by world-class data science.

What You’ll Do

  • Lead the design and implementation of ML models for mortgage-specific use cases, including credit underwriting, debt-to-income (DTI) validation, and automated appraisal reviews.
  • Provide technical oversight and end-to-end ownership of externally developed models, ensuring they meet rigorous internal standards for quality, performance, and scalability.
  • Present model performance and portfolio insights to senior leadership, translating complex data into actionable credit and business strategies.
  • Collaborate with Model Risk Management (MRM) and governance teams to ensure all models are developed with the technical rigor required to satisfy complex regulatory and compliance standards.
  • Partner with Product and Engineering teams to operationalize models, overseeing deployment, real-time monitoring, and seamless integration into core business systems.
  • Continuously explore and leverage in-house, external, and open-source ML frameworks to build the next generation of proprietary mortgage risk tools.

    • What You’ll Need

  • Master’s degree in Computer Science, Statistics, Mathematics, Physics, Engineering, or a quantitative field required. Ph.D. preferred.
  • 5+ years of direct experience with building, implementing, and deploying machine learning models within Home Loans or Home Equity lending environments.
  • Expert knowledge of statistical modeling and ML methods, including linear/logistic regression, ensemble methods (XGBoost/LightGBM), clustering, and outlier detection.
  • Strong programming skills in Python and SQL.
  • Hands-on experience with ML model implementation in production environments using tools such as AWS SageMaker, Git, Docker, and CI/CD pipelines.
  • Strong ability to distill complex technical methodologies into simple, persuasive terms for non-technical stakeholders.

    • Nice To Have

  • Proven track record in technical model documentation and navigating the Model Risk Management lifecycle.
  • Experience working in a cross-functional capacity across Product, Engineering, and Risk departments.
    • 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 an inclusive culture. 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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