Interested in this Data Scientist role at Charles Schwab?
Apply Now →Skills & Technologies
About This Role
Austin, TX ; Southlake, TX
Requisition ID 2026\-123030 Category Data Analytics and Strategy Position type Regular Pay range USD $145,000\.00 \- $205,000\.00 / Year Application deadline 2026\-06\-23
Your opportunity
--------------------
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’llbe part of a collaborative, technology\-forward environment that values curiosity, continuous learning, and thoughtful problem\-solving.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.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 organizationis acentralizedhub 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 toidentifyhigh impactuse 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.
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 scalechallenges.You’llbridge advanced research and robust engineering, owning theend‑to‑endlifecycle ofhigh‑impactmodels.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 toidentifyand translate advanced analytical techniques, applications, and strategies into practical production ready solutions.WhatYou’llDo* Get hands\-on with big dataas you analyze, interpret, extract insights, and produce innovative AI solutions that enable advanced decisioningtoleveragethe latest algorithms,state\-of\-the\-arttechniques, and tools.
- Design and buildend\-to\-endmachine learning systemsby defining scalable, reliable, and maintainable architectures that support data ingestion, feature generation, model training, evaluation, deployment, monitoring, and value measurement in production environments.
- Translate business strategy into technical executionby partnering with business stakeholders to converthigh\-levelbusinessobjectivesinto clear, actionable data science and AI solutions that address critical business and technology challenges.
- Set and elevate engineering standards for data sciencebyestablishingbest practices that treat data science as a rigorous engineering discipline, including modular code design, testing, version control, and production readiness.
- Advance technical capabilities in emerging areasby leading complex initiatives involving advanced machine learning, recommender systems,real\-timeandlow‑latencyinference, or other evolving technologies that require deep technicalexpertiseand comfort with ambiguity.
What you have
-----------------
Required Qualifications* 8\+ years of experience in data science and machine learning.
- Advanced degree (Master’sor PhD) in a quantitative field such as computer engineering, statistics, mathematics, physics, chemistry, or related discipline.
- 6\+ years ofhands\-onexperience using Python and SQL to developproduction‑grade, modular, and optimized code.
- Proven ability to convert business requirements into technical end\-to\-end machine learning solutions delivered againstroadmapmilestonesformultiplelines of business.
- Proven experience developing supervised and unsupervised machine learning solutions, with deliverysupported by documented evaluation metrics, performance tracking, and value measurement.
Experience in * ing natural language processing techniques to unstructured data with deliveryto production.
- Practical experience designing LLM solutions (such asretrieval‑augmentedgeneration, agent workflows, orfine‑tuning), deployed for internal use.
- Strong software engineering fundamentals, including version control, CI/CD, andMLOpspracticesforproduction deployments.
Preferred Qualifications* Strong background in statistics, forecasting, or causal inference.
- Hands\-onexperience architecting machine learning solutions within cloud ecosystems(GCP, AWS, Azure)
- Experience building,maintaining, andoptimizingdata pipelines that support machine learning workflows.
- ProvenexpertiseinMLOpsand productionmodelmonitoring.
- A demonstrated commitment to mentorship, including coaching senior data scientists or engineers and elevating team capability through feedback and code quality.
- Outstanding verbal and written communication skills withdemonstratedability to communicate effectively with all levels of the organization.
- Self\-starter with strong organizational skills, attention to detail, and desire to continually reevaluate existing products and processes.
- Comfort in a dynamic, fast\-moving environment, with a positive attitude, solid work ethic, and strongtrack recordof performance.
What’s in it for you
------------------------
At Schwab, you’re empowered to shape your future. We champion your growth through meaningful work, continuous learning, and a culture of trust and collaboration—so you can build the skills to make a lasting impact. Our Hybrid Work and Flexibility approach balances our ongoing commitment to workplace flexibility, serving our clients, and our strong belief in the value of being together in person on a regular basis.
We offer a competitive benefits package that takes care of the whole you – both today and in the future:
- 401(k) with company match and Employee stock purchase plan
- Paid time for vacation, volunteering, and 28\-day sabbatical after every 5 years of service for eligible positions
- Paid parental leave and family building benefits
- Tuition reimbursement
- Health, dental, and vision insurance
### Share:
- X
Eligible Schwabbies receive
-------------------------------
- Medical, dental and vision benefits
---------------------------------------
- 401(k) and employee stock purchase plans
--------------------------------------------
- Tuition reimbursement to keep developing your career
--------------------------------------------------------
- Paid parental leave and adoption/family building benefits
-------------------------------------------------------------
- Sabbatical leave available after five years of employment
-------------------------------------------------------------
Salary Context
This $145K-$205K range is above the median for Data Scientist roles in our dataset (median: $160K across 245 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,133 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Charles Schwab, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 868 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($175K) sits 12% below the category median. Disclosed range: $145K to $205K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Charles Schwab AI Hiring
Charles Schwab has 6 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, Data Scientist. Positions span Austin, TX, US, Westlake, TX, US, Southlake, TX, US. Compensation range: $94K - $247K.
Location Context
AI roles in Austin pay a median of $215,300 across 535 tracked positions. That's 7% above the national median.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (1,901) and senior (1,663) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $274,200 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.