Digital Product Analyst

Charles Schwab Inc.•Westlake, TX

7d•$85,500 - $130,000•Onsite

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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