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About the Role

As a Staff Data Scientist, you will play an essential part in advancing Schwab’s capabilities by driving the design, development, and implementation of innovative AI and machine learning solutions that address complex, enterprise scale challenges. You’ll bridge advanced research and robust engineering, owning the end‑to‑end lifecycle of high‑impact models. Successful candidates will work collaboratively across the organization with our business sponsors, development teams, and engineering partners. We are seeking a subject matter expert in all things AI, primed to identify and translate advanced analytical techniques, applications, and strategies into practical production ready solutions.

Responsibilities

Analyze, interpret, and extract insights from big data to produce AI solutions leveraging advanced algorithms, techniques, and tools.
Design and build end-to-end machine learning systems with scalable, reliable, and maintainable architectures for production environments.
Translate business strategy into technical execution by partnering with stakeholders to develop AI solutions for business and technology challenges.
Set and elevate engineering standards for data science, treating it as a rigorous engineering discipline.
Lead complex initiatives in emerging areas like advanced machine learning, recommender systems, and real-time inference.

Requirements

8+ years of experience in data science and machine learning.
6+ years of hands-on experience using Python and SQL for production-grade code development.
Proven ability to convert business requirements into end-to-end machine learning solutions.
Proven experience developing supervised and unsupervised machine learning solutions with documented metrics and value measurement.
Experience applying natural language processing techniques to unstructured data.
Practical experience designing and deploying LLM solutions for internal use.
Strong software engineering fundamentals, including version control, CI/CD, and MLOps practices.
Hands-on experience architecting machine learning solutions within cloud ecosystems (GCP, AWS, Azure).
Experience building, maintaining, and optimizing data pipelines for machine learning workflows.
Proven expertise in MLOps and production model monitoring.
Demonstrated commitment to mentorship, including coaching senior data scientists or engineers.
Outstanding verbal and written communication skills.
Self-starter with strong organizational skills and attention to detail.
Comfort in a dynamic, fast-moving environment.

Skills

Python
SQL
Natural Language Processing (NLP)
Large Language Models (LLM)
Retrieval-Augmented Generation (RAG)
Agent Workflows
Fine-tuning
Version Control
CI/CD
MLOps
Cloud Ecosystems (GCP, AWS, Azure)
Data Pipelines
Model Monitoring
Statistics
Forecasting
Causal Inference

Location

Remote

Work Type

Full-time

Experience Level

Staff
8+ years

Education Level

Advanced degree (Master’s or PhD) in a quantitative field

Salary/Compensations

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

About the Company

Schwab Data is the centralized organization that manages and enables the use of data as a strategic asset across Schwab, supporting enterprise analytics, platforms, and data-driven decision-making.
Schwab’s AI & Data Science organization is a centralized hub for delivering innovative production ready AI and machine learning solutions that drive measurable business outcomes across the firm.
The team partners with Schwab business units to identify high impact use cases, pilot innovative analytical solutions, and transition successful models into enterprise level production systems.
Our mission is to accelerate the adoption of AI as a strategic product capability—ensuring models are scalable, reusable, governable, and continuously delivering value.