Senior Manager, Product Analytics

Your Opportunity

Schwab remains committed to providing increased visibility to career growth opportunities and job requirements. This posting announcement is part of increased transparency and while all qualified applicants will be reviewed and considered, this organization has a preferred candidate identified for this role. 

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. 

Digital Retail is a group within our Retail business that leads strategy and delivery for all of Schwab’s retail digital channels and experiences (Schwab.com and the Schwab mobile app). In Digital Retail we empower investors to Own Their Tomorrow by delivering industry-leading, exceptional digital experiences, supporting ~10 million unique monthly users and 150+ million monthly logins. 

Role Summary

Reporting to the Director of Strategic Planning & Insights on the Digital Retail team, the Senior Manager, Journey Performance & Predictive Analytics builds and operates the analytics engine that measures how our products and journeys perform and predicts where clients will struggle next. You will own product outcome measurements, end-to-end journey performance intelligence, an automated executive insights hub, and AI-powered and predictive analytics, bringing in-house the measurement and insight capabilities. This role is expected to build and scale capabilities through automation, AI, and agentic workflows. Success will be measured by the ability to create repeatable, self-service intelligence and insight generation with minimal manual effort rather than through organizational growth. This is a hands-on role for a data leader who can define outcome frameworks, build journey and health dashboards, automate executive reporting, and apply predictive techniques to get ahead of client friction. 

Key Responsibilities

Product & Journey Performance Measurement

• Define and operationalize a standard product outcome framework with adoption and utilization views and release impact tracking, so teams can consistently measure whether what they ship works. 

• Build end-to-end journey performance intelligence, including journey dashboards, abandonment tracking, and health scoring, that show how clients move through Schwab.com and the mobile app. 

• Establish trusted, comparable performance measurement that replaces one-off analyses and reduces reliance on external support. 

Predictive & AI-Powered Analytics

• Help establish an executive insights hub for senior Digital Retail and Retail leadership. 

• Ensure reporting connects performance data to implications and recommended actions, not just metrics. 

• Develop predictive client signals, including emerging issue detection, dissatisfaction forecasting, and friction alerts, to get ahead of problems before they affect clients. 

• Build AI-powered insight summaries, including natural language querying, auto-generated narratives, and root-cause explanations, to make insight faster and more accessible. 

• Define measurable success criteria for AI and predictive efforts, including accuracy, time saved, and improved decision speed and quality. 

Data Enablement, Partnership & Governance

• Partner with Product, Technology, Data, and external analytics partners to source, validate, and connect the data behind journey and outcome measurement. 

• Enhance the organization’s ability to self-serve performance data and make data-driven decisions, bridging gaps across the Digital organization. 

• Operate with appropriate discipline for a regulated environment, including validation of outputs, strong judgment on data handling, and adherence to governance and control expectations. 

What Success Looks Like

• Digital Retail measures product and journey performance consistently, with trusted, comparable metrics owned in-house. 

• Executive reporting is automated, timely, and decision-oriented, with less manual effort. 

• Predictive signals help teams get ahead of client friction rather than reacting to it. 

• The organization relies less on external partners for core measurement and insight. 

• AI and automation move recurring analytics from manual work to measurable efficiency and quality.

What you have

Required Qualifications

• 7–10+ years of experience in data and analytics, product analytics, business insights, or performance measurement, ideally within financial services or another regulated industry 

• Proven ability to define outcome and measurement frameworks and build journey, adoption, and health dashboards that stakeholders can self-serve 

• Strong data fluency, including comfort with behavioral and performance data, funnel and abandonment analysis, and translating findings into decisions 

• Experience with data visualization and business intelligence platforms (e.g., Tableau, Adobe Analytics) 

• Hands-on experience with predictive or advanced analytics techniques and a practical understanding of how to apply them responsibly 

• Exceptional attention to detail, with a track record of producing accurate, leadership-ready reporting 

• Strong executive communication and storytelling skills, with the ability to move from analysis to implications, recommendations, and action 

• Demonstrated ability to operate in ambiguous environments and drive work forward with limited direction 

• Comfort using AI-powered tools, including Copilot-style tools, to accelerate analysis and reporting 

• Bachelor’s degree required 

Preferred Qualifications

• Experience insourcing or building analytics capabilities previously delivered by external consulting or vendor partners 

• Experience automating recurring executive or operational reporting 

• Experience with journey analytics, conversion and abandonment optimization, or experience health scoring 

• Demonstrated ability to leverage AI, automation, and workflow orchestration to scale output and impact without proportional increases in headcount. 

• Prior experience working with OKRs, KPIs, and operational reporting 

• Experience applying AI or machine learning to forecasting, anomaly detection, or natural language insight generation 

• Financial services industry experience