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About This Role
Austin, TX ; Southlake, TX
Requisition ID 2026\-123766 Category Data Analytics and Strategy Position type Regular Pay range USD $150,000\.00 \- $185,000\.00 / Year Application deadline 2026\-07\-18
Your opportunity
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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.
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).
Organization / Role Description
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Schwab’s AI \& Data Science organization is the centralized hub for delivering responsible, production‑grade 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 compliant, resilient 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 in a highly regulated environment.
As a Senior AI \& 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.
### Key Responsibilities
- Get hands\-on with big data as you analyze, interpret, extract insights, and produce innovative AI solutions that enable advanced decisioning leveraging the latest algorithms, state\-of\-the\-art techniques, and tools.
- Design and build end‑to‑end machine learning systems by 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 execution by partnering with business stakeholders to convert high‑level business objectives into clear, actionable data science and AI solutions that address critical business and technology challenges.
- Set and elevate engineering standards for data science by establishing best 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 areas by leading complex initiatives involving advanced machine learning, recommender systems, real‑time and low‑latency inference, or other evolving technologies that require deep technical expertise and comfort with ambiguity.
What you have
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### Required Qualifications
- 8\+ years of experience in data science and machine learning.
- Advanced degree (Master’s or PhD) in a quantitative field such as computer engineering, statistics, mathematics, physics, chemistry, or related discipline.
- 6\+ years of hands‑on experience using Python and SQL to develop production‑grade, modular, and optimized code.
- Proven ability to convert business requirements into technical end\-to\-end machine learning solutions delivered against roadmap milestones for two or more lines of business.
- Proven experience developing supervised and unsupervised machine learning solutions, with delivery of three or more distinct models supported by documented evaluation metrics, performance tracking, and value measurement.
Experience in * ing natural language processing techniques to unstructured data with at least one solution delivered to production.
- Practical experience designing LLM solutions (such as retrieval‑augmented generation, agent workflows, or fine‑tuning), including at least one LLM system deployed for internal use.
- Strong software engineering fundamentals, including version control, CI/CD, and MLOps practices, demonstrated through three or more production deployments.
### Preferred Qualifications
- Experience working in financial services or other highly regulated industries.
- Strong background in statistics, forecasting, or causal inference.
- Hands‑on experience architecting machine learning solutions within cloud ecosystems.
- Experience building, maintaining, and optimizing data pipelines that support machine learning workflows.
- Experience developing large‑scale recommender or personalization systems.
- 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 with demonstrated ability 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 strong track records of performance.
In addition to the salary range, this role is also eligible for bonus or incentive opportunities.What’s in it for you
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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
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- Medical, dental and vision benefits
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- 401(k) and employee stock purchase plans
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- Tuition reimbursement to keep developing your career
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- Paid parental leave and adoption/family building benefits
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- Sabbatical leave available after five years of employment
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Salary Context
This $150K-$185K range is above the median for Data Scientist roles in our dataset (median: $155K across 226 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 3,708 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 $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($167K) sits 13% below the category median. Disclosed range: $150K to $185K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Charles Schwab AI Hiring
Charles Schwab has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span San Francisco, CA, US, Austin, TX, US, Southlake, TX, US. Compensation range: $139K - $250K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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