Senior Machine Learning Engineer
at SoFi
CA - San Francisco
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The Role
The Risk Data Science team is looking for a Senior Machine Learning Engineer/Data Scientist to develop advanced machine learning models, guide measurement, strategy, and data-driven decision making to support various credit risk and operational areas for SoFi Credit Card products. The person will work closely with Credit, Risk, Product, Engineering, and Operations teams to design solutions to support acquisition, account management, and loss mitigation. These tasks involve applying state-of-the-art machine learning methodologies, deploying models into production, and developing complex business rules to solve business problems. 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 machine learning models that support various credit and operational procedures including but not limited to underwriting, account management, and loss mitigation.
- Implement scalable model pipelines leveraging modern ML platforms (e.g., AWS SageMaker) and best practices in version control (Git), testing, and CI/CD.
- Partner with Product and Engineering teams to operationalize models, including deployment, monitoring, and integration into business systems.
- Collaborate with the Model Risk Management team to ensure models are developed with rigor that satisfy Model Risk Management and Governance requirements.
- Present model performance and insights to Credit, Risk, and Business Unit leaders.
- Explore and leverage in-house, external, and open-source machine learning frameworks and algorithms.
What You’ll Need
- Bachelor’s degree in Computer Science, Statistics, Mathematics, Physics, Engineering, or a quantitative field required. Master’s or Ph.D. preferred.
- 3+ years of relevant work experience with building, implementing, and deploying machine learning models.
- Excellent knowledge of machine learning and statistical modeling methods for supervised and unsupervised learning, including (but not limited to) linear regression, logistic regression, decision tree, ensemble methods, clustering, outlier detection, novelty 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.
- Effective communication skills and ability to explain complex models in simple terms.
Nice To Have
- Experience in a financial organization.
- Experience with model documentation and delivering effective verbal and written communication.
- Experience working closely with Product, Engineering, and Model Risk Management teams.