Staff Marketing Data Scientist, Machine Learning

SoFi•San Francisco, CA

About The Position

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 Marketing Data Science (MDS) team is seeking an experienced Machine Learning professional to join the group. This team is essential for supporting SoFi's Marketing organization by providing data-driven decision-making through rigorous analytics, modeling, experimentation, and measurement. The primary responsibility of this role is to build and scale machine learning models that drive marketing and growth across SoFi's product suite. The focus will be on developing predictive models for key areas: acquisition, conversion, retention, and customer lifetime value (LTV), utilizing behavioral, transactional, and credit data. This role requires strong collaboration across teams to identify the business needs, manage the end-to-end model development cycle, implement the model in production, and monitor performance, while maintaining strict compliance with all regulatory standards.

Requirements

Bachelor's degree in Computer Science, Math, Physics, Engineering, or a quantitative field required
8+ years of experience in machine learning, data science or applied AI, ideally in marketing, fintech, or consumer tech.
Strong proficiency in Python, SQL, and ML frameworks (scikit-learn, PyTorch, TensorFlow).
Demonstrated experience with experimentation and hypothesis testing.
High intellectual curiosity and a quick learner of new technical skills.
Ability to initiate and drive projects independently to completion, and ability to clearly communicate technical information to diverse audiences.
Strong relationship-building and collaborative skills.
Ability to thrive in a dynamic, cross-functional environment with keen attention to detail.

Nice To Haves

Master's degree preferred.
Familiarity with ML governance, model monitoring, and compliance frameworks (MRM, Fair Lending, etc.) is a plus.
Experience with LLMs, prompt engineering, and AI application development preferred.

Responsibilities

Design, develop, and deploy machine learning models to optimize customer acquisition, onboarding, and lifecycle engagement across financial products (e.g., loans, credit cards, money, invest, crypto and etc).
Build predictive models for critical business outcomes, including Customer Lifetime Value (LTV), conversion propensity, cross-sell/upsell effectiveness, and retention across various channels (e.g., direct mail, email, in-app, Operations).
Leverage structured and unstructured data (e.g., behavioral signals, transaction data, credit attributes) to drive audience segmentation and personalization at scale. Develop and maintain a robust feature store to accelerate the end-to-end model development lifecycle.
Build experimentation frameworks to support A/B testing and measure the incremental business impact of the models. Work with the other team members to incorporate the models in marketing/campaign strategies.
Collaborate with Fair Lending (FL) and Model Risk Management (MRM) to ensure the models and targeting strategies adhere to fair lending laws, privacy regulations, and responsible marketing practices.
Support the efficient scoring and implementation of models through both real-time and batch processes, ensuring the timely availability of scores for marketing execution and deep analytics.
Monitor model performance with a focus on business outcomes (conversion, retention, revenue, risk-adjusted returns) and continuously iterate based on insights.
Partner closely with team members within MDS and key business stakeholders on defining business requirements, prioritizing the model pipeline, providing development updates, and monitoring performance.
Stay at the forefront of modeling, data, and AI innovations to support scalable marketing growth in financial services.