Digital Product Analyst

Your Opportunity At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together.

We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s).

Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.

At Schwab, we are focused on creating simple, relevant, and client-centered digital experiences. The Experimentation and Personalization team helps product and business partners learn faster, make evidence-based decisions, and deliver experiences that better reflect client needs. We are seeking a hands-on analytics leader who can connect program strategy, measurement, data, and execution across our experimentation and personalization capabilities.

The Digital Product Analyst (individual contributor) will establish a unified view of program and platform performance, measure the utilization and business impact of delivered experiences, strengthen experimentation practices, and advance audience and model optimization. The Manager will work across product, analytics, data science, technology, and business teams to define standards, close data gaps, and create scalable reporting and decision-support capabilities. What You’ll Do Lead program measurement and insight

Own the measurement strategy for experimentation and personalization, including program health, platform utilization, adoption, outcomes, and business impact Define consistent metrics, KPIs, measurement frameworks, calculation methods, and reporting standards across programs and participating teams Build and maintain dashboards, recurring reports, and executive-ready portfolio views that translate activity and results into decisions and priorities Develop approaches to assess incremental impact and ROI, while clearly documenting assumptions, limitations, and data quality considerations Identify trends, gaps, and opportunities that shape program strategy, capability roadmaps, and investment decisions

Advance experimentation analytics

Partner with product and business teams to define testable hypotheses, primary and secondary metrics, guardrails, sample-size and duration assumptions, and analysis plans Analyze A/B tests, multivariate tests, feature experiments, and other controlled evaluations using appropriate statistical methods Monitor live experiments for data quality, allocation, instrumentation, and performance issues without compromising test integrity Translate completed test results into clear recommendations, reusable insights, and implications for product and experience roadmaps Establish scalable standards, templates, and best practices for experiment design, measurement, reporting, and knowledge sharing

Strengthen personalization analytics and optimization

Define how personalized experiences, audiences, decision rules, recommendations, and journeys will be measured across digital channels Lead analysis of audience reach, eligibility, utilization, engagement, conversion, and downstream client or business outcomes Provide analytical input to audience design, model performance, test-and-control strategies, and ongoing optimization of decisioning approaches Manage or coordinate audience creation and lifecycle practices, including definitions, documentation, validation, refresh expectations, and performance monitoring Create a feedback loop that uses experience outcomes to inform model, audience, content, and journey improvements

Build the data and reporting foundation

Integrate and reconcile data from experimentation, digital analytics, personalization, audience, and business reporting platforms using SQL, Python, APIs, and enterprise data tools Identify instrumentation, taxonomy, identity, data quality, and