Sr Manager, AI & Data Science Lead

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. 

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).

At Schwab, you will build a rewarding career while making a difference in the lives of our millions of clients. Here, innovative thinking meets creative problem solving as we work together to challenge the status quo. You’ll be part of a collaborative, technology-forward environment that values curiosity, continuous learning, and thoughtful problem-solving. Schwab Technology Services (STS) enables innovative and reliable technology products that power how clients manage their money, supporting Schwab’s commitment to expanding access to investing and financial planning.  Joining Schwab means joining a company committed to transforming the financial industry and putting clients at the center of everything we do.

Schwab’s AI & Data Science team partners with business units across the firm to deliver quantitative decision support. This role sits within a broader AI & Data Science organization whose portfolio spans marketing measurement and optimization, journey analytics, and natural-language-processing (NLP) solutions supporting client, marketing, and operational decision-making. Our mission is to help our business partners understand client behavior and translate those insights into product, marketing, and operational decisions. 

The AI & Data Science organization is the centralized hub for delivering production-ready analytical and machine-learning solutions that drive measurable business outcomes across the firm. The portfolio spans a range of quantitative products — including measurement and optimization capabilities (e.g., Marketing Mix Optimization), journey analytics, and natural-language-processing systems — all under active modernization onto a governed, cloud-based platform aligned to enterprise data and compliance standards. 

As the Sr. Manager, AI & Data Science Lead, you will serve as the technical lead for the broader AI & Data Science organization, providing hands-on technical direction, architecture review, and code-review depth across the full project portfolio — spanning measurement and optimization, journey analytics, and NLP-based products. You will elevate engineering and modeling standards across the org, guide the modernization of legacy analytical workflows onto the enterprise platform (Dataiku with data via the central landing zone), and partner with business stakeholders across marketing, product, and operations. You will also develop the next generation of data scientists through hands-on coaching, code review, and technical mentorship.

• Serve as the technical lead across the broader AI & Data Science organization’s project portfolio — spanning marketing measurement (including MMO), journey analytics, and NLP-based products — providing hands-on technical direction, architecture review, and code-review depth so that each project meets a consistent engineering and modeling bar. 
• Own the technical direction of individual product lines within the portfolio (e.g., Marketing Mix Optimization) end-to-end — from model design through recurring refresh cycles, validation, and executive-facing readouts — ensuring stakeholders receive timely and defensible guidance. 
• Design and evolve automation pipelines that support recurring model rebuild and reporting cycles — data ingestion, feature construction, model fitting (including Bayesian methods such as PyMC where appropriate), diagnostics, and stakeholder outputs — with strong reproducibility and audit trails. 
• Translate business strategy into technical execution by partnering with stakeholders across marketing, product, operations, engineering, and finance to convert business questions into clear modeling requirements, evaluation criteria, and delivery milestones. 
• Set and elevate engineering standards across the AI & Data Science org — modular design, versioning, testing, reproducible environments, and production readiness — treating quantitative modeling as a rigorous engineering discipline across all workstreams. 
• Advance the org’s technical capabilities by leading complex initiatives in areas such as Bayesian modeling, causal inference, incrementality testing, and natural language processing, and by evaluating emerging methods (e.g., hierarchical priors, geo-experiments, uplift modeling, LLM-based components) for adoption into production systems. 
• Lead the modernization of legacy analytical workflows onto the enterprise platform (Dataiku with data sourced from the central landing zone), in support of organizational OKRs and to strengthen data lineage, governance, and compliance posture. 
• Strengthen model quality and resiliency across the portfolio by running regular code reviews on projects org-wide, driving revision-based adoption of best practices, and promoting cross-training so team members can fluidly contribute across workstreams. 
• Mentor and develop data scientists across the AI & Data Science org through continuous, individualized coaching, hands-on review of analytical work, and structured onboarding of new hires — building both technical depth and confidence on complex modeling tasks. 

What you have

To ensure that we have fulfilled our promise of "challenging the status quo," this role has specific qualifications that successful candidates should have.

Required: 

• 10+ years of experience in data science, quantitative analytics, or applied machine learning, including delivery of production-grade analytical solutions across more than one business domain. 
• Advanced degree (MS or PhD) in a quantitative field such as statistics, economics, operations research, computer science, mathematics, physics, or a closely related discipline. 
• 7+ years of hands-on experience using Python and SQL to develop production-grade, modular, and optimized code, including experience refactoring legacy analytical code into maintainable production pipelines. 
• Proven ability to convert complex business requirements into end-to-end analytical solutions delivered against roadmap milestones for two or more distinct business areas. 
• Proven experience developing statistical, econometric, or Bayesian solutions applied to business problems (examples include measurement, attribution, optimization, and causal inference), with delivery of three or more distinct models supported by documented evaluation metrics, back-testing, and business-value measurement. 
• Demonstrated ability to lead automation of an analytical workflow end-to-end, including scheduled data refresh, model rebuild, diagnostics, and stakeholder-facing reporting, with measurable reduction in manual effort and cycle time. 
• Practical experience owning migration of an analytical workflow onto a governed enterprise platform (e.g., Dataiku or comparable), including sourcing data from a central data platform and meeting lineage, governance, and compliance requirements. 
• Strong software engineering fundamentals — version control, CI/CD, testing, and MLOps practices — demonstrated through three or more production or production-adjacent deployments.

Preferred Qualifications 

• Experience working in financial services or another highly regulated industry, with familiarity operating within enterprise data governance and model risk expectations. 
• Strong background in Bayesian modeling (e.g., PyMC), econometrics, or causal inference, with familiarity across techniques such as marketing mix modeling, multi-touch attribution, incrementality testing, or NLP-based solutions. 
• Hands-on experience architecting analytical or machine-learning solutions within cloud or enterprise data-science ecosystems and integrating with a central data platform 
• Experience building, maintaining, and optimizing data pipelines that support recurring model refresh cycles (e.g., quarterly rebuilds) and stakeholder-facing reporting. 
• Experience partnering with business stakeholders across multiple lines of business and presenting technical findings and trade-offs to senior leaders. 
• Demonstrated commitment to mentorship — coaching data scientists, onboarding new hires, and elevating team capability through structured feedback, code review, and knowledge sharing. 
• Experience providing technical leadership across a portfolio of analytical products spanning multiple methodologies (e.g., statistical and Bayesian modeling, machine learning, natural language processing). 
• Outstanding verbal and written communication skills, with a track record of translating complex quantitative results into clear recommendations for non-technical executive audiences. 
• Self-starter with strong organizational skills, attention to detail, and a bias toward continually reevaluating existing models, processes, and tooling for improvement. 
• Comfort operating in a dynamic, cross-functional environment across business, analytics, and engineering partners, with a positive attitude and strong track record of on-time delivery.

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