Head of Product Data Science

SoFi   •  

San Francisco, CA

Industry: Finance & Insurance


11 - 15 years

Posted 57 days ago

This job is no longer available.


We are seeking an experienced Data Executive to lead and grow the Product Data Science team in the Data Organization. This team is driving development of SoFi products through data and enables multiple business units who are the main customers of the team. Its day-to-day activities include: (1) product funnel construction, (2) accounting for user growth, (3) experimentation and (4) identification of strategic data insights and growth opportunities. This is an exciting senior leadership opportunity to directly influence SoFi members and help them get their money right as well as to meaningfully drive growth of the company.


1) Execution

Build an understanding of Member experience through funnel, retention, and lifecycle analysis. Represent the voice of Members and recommend product strategy or feature enhancements to improve key product metrics.

Generate insights and inform the development of business strategies through deep data analysis.

Design A/B tests and with product/engineering to execute A/B test experiments, analyze and extract causality and recommend product feature enhancements.

Build models based on acquired data to predict conversion, churn and retention.

Set up sprints with cross functional stakeholders to ensure alignment and transparency of work being delivered in upcoming weeks to prioritize high value work and reduce ad-hoc requests.

Own daily or weekly standups with cross-functional stakeholders to synthesize product performance and action items for partners in marketing, risk, and operations.

Own self-serve portals such as dashboards, reports, and datamarts so our partners are able to perform exploratory analysis without data science bottlenecking.

Continuously monitor and be accountable for the quality of data in all deliverables of the team.

2) Technology development:

Try, evaluate and recommend technology platforms (both premium and open source) to ensure technical ecosystem is sufficient for data scientists to perform tasks.

Contribute to feature improvements for data ops, data warehouse, and growth engineering teams to constantly improve data accuracy and availability.

Discover gaps in instrumentation and drive toward coverage by partnering with product and engineering teams to ensure key actions in customer journey are tracked properly.

Identify gaps in our overall data pipelines and architecture approach; partner with data architect and delivery teams to address these gaps.

3) Stakeholder Alignment

As single point of contact, own end-to-end data science/analytics function for business unit partners and create synergies between Product Data Science teams. Ensure that other functional data science partners are delivering work while maintaining accountability.

Set a compelling vision and prioritize work based on business unit objectives, Product initiatives and Data Science Team goals.

Partner closely with business unit teams to design operating model in order to operate efficiently with each business unit and cross-functional teams.

4) Team Building and Career Development:

Mentor team through democratization of best analytical practices.

Attract, hire and retain top tier data scientists to SoFi through LinkedIn, University connections, and personal network.

Build career tracks for both individual contributor and manager tracked teammates.

Provide developmental feedback while maintaining a psychologically safe environment to ensure teammates grow.

Promote career development through on-campus classes or external courses, conferences and industry events.


M.B.A. or Master's or Ph.D. in Computer Science, Statistics, Operations Research, Engineering, Mathematics or a related quantitative field;

At least 10 years of people leadership and management experience;

At least 10 years of financial services experience working in the areas of data science and analytics;

Proven track record of leading product data science/analytics teams with emphasis on funnel, retention and lifecycle analysis;

Expert knowledge of statistical methodologies and deep understanding of modeling and machine learning methods;

Programming skills in Python and/or R;

Understanding of cloud platforms and related technologies;

Ability to work in a dynamic, cross-functional environment, with a strong attention to detail;

Effective communication skills and ability to explain complex technical solutions in simple terms;

Strong relationship-building and collaborative skills;

Exceptional problem-solving skills.