Senior AI & Data Scientist — Production ML & AI Solutions

Charles Schwab; Ann Arbor (MI); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

401(k) with company match

Employee stock purchase plan

Sabbatical after 5 years

Parental leave and family benefits

Tuition reimbursement

Health, dental, and vision insurance

Job summary

Charles Schwab is seeking an Senior AI & Data Scientist to drive the design, development, and deployment of enterprise-scale AI/ML solutions. You will partner with business sponsors and engineering teams to deliver value from data across Schwab’s platforms.

You will own end-to-end model lifecycle, apply advanced ML techniques including NLP and LLMs, and ensure production readiness with robust MLOps practices.

Qualifications

• 8+ years of data science and ML experience.
• Advanced degree in a quantitative field.
• 6+ years of Python and SQL production-grade coding.
• Proven end-to-end ML solutions across multiple lines of business.
• Experience with supervised/unsupervised ML and production metrics.

Responsibilities

• Develop end-to-end ML systems from data ingestion to deployment and monitoring.
• Translate business objectives into data science solutions aligned to roadmaps.
• Establish engineering standards for data science (modular code, testing, version control).
• Lead complex ML initiatives including real-time/inference and recommender systems.
• Drive adoption of AI as a production capability in a regulated environment.

Skills

Python

SQL

Production ML

ML Ops

NLP

LLM

Data analysis

Big data

Software engineering

CI/CD

Education

Master's or PhD in a quantitative field

Tools

Git

CI/CD tools

ML platforms

Job description

Charles Schwab is seeking an Senior AI & Data Scientist to drive the design, development, and deployment of enterprise-scale AI/ML solutions. You will partner with business sponsors and engineering teams to deliver value from data across Schwab’s platforms.

You will own end-to-end model lifecycle, apply advanced ML techniques including NLP and LLMs, and ensure production readiness with robust MLOps practices.