Marketing Data Scientist, Optimization

The role:

SoFi is seeking a highly motivated Data Scientist to join the Marketing Data Science (MDS) team. The MDS team plays a crucial role in enabling data-driven decisions across SoFi's Marketing organization through robust analytics, modeling, experimentation, and measurement. 

This exciting new role will be part of the Optimization hub and will support both TV/DV media-specific optimization and broader marketing budget planning, with roughly half of the work focused on media analytics, forecasting, and optimization, and the other half focused on holistic marketing budget forecasting and scenario planning across brand, sponsorships, product marketing, and all marketing channels (TV, DV, DM, social, display etc.).

The role is execution-focused, with ownership of recurring analytical deliverables and workflows. The IC2 will work closely with senior data scientists and marketing stakeholders, with a clear growth path toward independently owning forecasting, optimization analyses, and stakeholder partnerships over time.

What you’ll do: 

• Own and maintain recurring dashboards and analytical deliverables that support media performance monitoring and broader marketing spend diagnostics across channels.
• Partner with Media, Brand Marketing, and Product Marketing to support day-to-day analytics, deep dives, and planning-related questions.
• Support quarterly and annual marketing forecasts using SoFi’s in-house Marketing Mix Modeling (MMM) and related forecasting frameworks.
• Run MMM pipelines and refreshes, including data preparation, quality checks, model execution, and output validation.
• Assist in interpreting MMM outputs and translating results into insights that inform media and marketing investment decisions.
• Support media allocation and optimization analyses (e.g., TV vs. Digital, upper- vs. lower-funnel tradeoffs), including scenario modeling and sensitivity analysis.
• Contribute to holistic marketing budget optimization and scenario planning across brand, sponsorships, product, and performance marketing investments.
• Support cross-channel budget tradeoff analyses to evaluate the impact of spend shifts across media types and marketing initiatives.
• Contribute to ongoing improvement of MMM models and forecasting tools, including feature development, testing, automation, and documentation.
• Collaborate closely with senior data scientists to improve forecast accuracy, model robustness, and the usability of optimization outputs for stakeholders.
• Maintain clear documentation of models, assumptions, and analytical workflows to support long-term maintainability.

What you’ll need:

• Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, Engineering, or a related quantitative field.
• 2–4 years of experience in data science, analytics, or applied modeling roles supporting marketing, media, growth, or product analytics.
• Strong proficiency in SQL and Python, with experience building analytical workflows and working with large, structured datasets.
• Experience with forecasting, causal modeling, or Marketing Mix Modeling (MMM), including running model pipelines and supporting model interpretation.
• Familiarity with marketing performance measurement across a broad range of channels, including upper- and lower-funnel investments.
• Experience building and maintaining dashboards or recurring analytical deliverables using BI tools (e.g., Tableau or equivalent).
• Comfort working in a production analytics environment, including data validation, model QA, and ongoing maintenance.
• Ability to translate analytical results into clear insights for non-technical stakeholders, with support from senior team members.
• Strong attention to detail, structured problem-solving skills, and comfort working with ambiguous planning questions.
• Collaborative mindset and ability to work effectively with cross-functional partners across Marketing.
• Curiosity and motivation to grow toward greater ownership of forecasting, optimization, and stakeholder-facing work over time.