Senior AI & Data Scientist: Lead Generative AI & ML

Charles Schwab; Austin (TX); On-site; USD 150,000 - 210,000; Full time

Benefits offered by this job

401(k) with company match

Employee stock purchase plan

Sabbatical after five years

Tuition reimbursement

Health, dental, and vision insurance

Job summary

Charles Schwab seeks a Senior AI & Data Scientist to lead advanced analytics, ML, and generative AI initiatives in Austin. You will guide a team across product, engineering, risk, and operations to deliver scalable, responsible AI solutions.

You’ll partner to define AI roadmaps, translate business needs into data-driven strategies, and move projects from concept to production while upholding governance and MLOps practices.

Qualifications

• Bachelor’s degree in a quantitative field required
• 8+ years in Data Science/ML/AI or related
• 3+ years mentoring or guiding AI/ML practices
• 3+ years deploying ML/AI solutions in production
• Experience with NLP, Generative AI, or related fields
• Proficiency in Python, SQL, and modern AI/ML tools
• Ability to translate business needs into data-driven solutions
• Experience leading cross-functional projects and stakeholders

Responsibilities

• Lead and develop a team of AI/Data Science professionals
• Partner across business, product, engineering, risk, and operations to define AI roadmaps
• Move high-impact AI solutions from concept to production and govern deployment

Skills

Python

SQL

AI/ML frameworks

Communication

Stakeholder management

Education

Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or related field

Master’s or Ph.D. in related field

Tools

Cloud platforms (Azure/AWS/Google Cloud)

Job description

Charles Schwab seeks a Senior AI & Data Scientist to lead advanced analytics, ML, and generative AI initiatives in Austin. You will guide a team across product, engineering, risk, and operations to deliver scalable, responsible AI solutions.

You’ll partner to define AI roadmaps, translate business needs into data-driven strategies, and move projects from concept to production while upholding governance and MLOps practices.