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 source-integration gaps that affect measurement or decisioning

• Define program-level data requirements and partner with Schwab Data and technology teams to prioritize data ecosystem improvements

• Establish traceable, repeatable, and well-documented analytical workflows that can scale across business units and digital channels

• Create and maintain data definitions, source documentation, quality checks, and reporting controls appropriate for a highly governed environment

Influence across a matrixed organization

• Serve as the central analytics partner for Experimentation and Personalization, coordinating across business-unit analysts, product managers, data scientists, engineers, strategy teams, and platform partners

• Build alignment on shared metrics, standards, ownership, and priorities while respecting the needs of individual use cases and business areas

• Communicate complex analytical concepts, tradeoffs, and findings with clarity to technical, business, and executive audiences

• Coach partners on measurement and analytical best practices and promote disciplined, client-focused use of data

• Operate independently through ambiguity, balancing hands-on analysis with program structure, governance, and long-term capability development

What you have

Success in This Role

• Leaders and program teams have a trusted, consistent view of experimentation and personalization performance

• Experiments and personalized experiences are measured with clear standards and produce actionable, reusable learning

• Audience and model decisions are informed by transparent performance data and disciplined optimization

• Data gaps and ecosystem needs are clearly defined, prioritized, and advanced with the right partners

• Program reporting shifts from fragmented, use-case-level views to scalable portfolio and platform insight

Required Qualifications

• Bachelor’s degree in statistics, data science, mathematics, economics, computer science, business analytics, or a related field, or equivalent practical experience

• 5+ years of experience in data analysis, digital analytics, product analytics, experimentation analytics, decision science, or a related discipline

• Demonstrated experience designing measurement frameworks, defining KPIs, and building dashboards or recurring performance reporting

• Hands-on experience analyzing controlled experiments and applying statistical concepts such as hypothesis testing, confidence intervals, power, sample size, and minimum detectable effect

• Proficiency with SQL and at least one analytical programming language, preferably Python, for data extraction, transformation, validation, automation, or statistical analysis

• Experience working with APIs and combining data from multiple platforms or enterprise sources

• Ability to translate complex data into concise insights, recommendations, and compelling narratives for business and technical stakeholders

• Proven ability to lead cross-functional work, build consensus, and influence outcomes in a highly matrixed organization

• Strong attention to detail, documentation, organization, and analytical quality

Preferred Qualifications

• Graduate-level education or advanced coursework in statistics, experimentation, analytics, or a related quantitative field

• Experience with Adobe Analytics and digital behavioral data

• Experience with LaunchDarkly, Optimizely, Adobe Target, or another experimentation or feature-management platform

• Experience supporting personalization, audience management, recommendation systems, customer decisioning, or next-best-action capabilities

• Experience evaluating model or audience performance and partnering with data science teams on optimization

• Experience with business intelligence tools such as Power BI, Tableau, or equivalent dashboarding platforms

• Experience with cloud data platforms, enterprise data warehouses, or data transformation workflows

• Experience in digital product, mobile applications, financial services, or another regulated enterprise environment

In addition to the salary range, this role is also eligible for bonus or incentive opportunities.