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About This Role
Location San Antonio, Texas
Job ID
R0119705
Date posted
08/08/2026
Why USAA?
At USAA, our mission is to empower our members to achieve financial security through highly competitive products, exceptional service and trusted advice. We seek to be the \#1 choice for the military community and their families.
Embrace a fulfilling career at USAA, where our core values – honesty, integrity, loyalty and service – define how we treat each other and our members. Be part of what truly makes us special and impactful.
We are proud to support active\-duty military spouses. USAA roles may offer remote or hybrid flexibility for active\-duty military spouses consistent with applicable policy and business needs.
The Opportunity
As an experienced AI Data Scientist in the Technology organization at USAA, you will work within our innovative Data Science team to tackle a broad and evolving spectrum of business targets to provide outstanding impacts for our membership, leveraging both structured and unstructured data through traditional pillars of operations research such as simulation, optimization, and machine\-learning techniques, as well as a heavy emphasis on cutting\-edge technologies with generative AI and large language models. You’ll collaborate with other data scientists to improve USAA's tooling, expanding the company's library of internal packages and applications, and validate the results and stability of models before being pushed to production at scale.
This team is the backbone of the next generation of AI modeling at USAA, and we hope you join us on the frontier!
We offer a flexible work environment that requires an individual to be in the office 4 days per week. This position can be based in one of the following locations: San Antonio, TX, Plano, TX, or Phoenix, AZ. Relocation assistance is not available for this position.
What you'll do:
- Gather, interpret, and manipulate structured and unstructured data to enable advanced analytical solutions for the business.
- Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
- Select the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
- Develop and deploy models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
- Compose technical documents for knowledge persistence, risk management, and technical review audiences.
- Assess business needs to propose/recommend analytical and modeling projects to add business value.
- Participate in the prioritization of analytics and modeling problems/research efforts with business and analytics leaders.
- Contribute to the development of a robust library of reusable, production\-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
- Translate business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non\-technical business colleagues with focus on business action and recommendations.
- Work closely with Data Engineering, IT, the business, and other internal stakeholders to deploy production\-ready analytical assets that are aligned with the customer's vision and specifications while being consistent with modeling best practices and model risk management standards.
- Maintain awareness of cutting\-edge techniques.
- Actively seek opportunities and materials to learn new techniques, technologies, and methodologies.
- Ensure risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.
What you have:
- Bachelor’s degree in mathematics, Computer Science, Statistics, Science, Engineering, or quantitative field; OR 4 years of relevant education and/or experience; and
- 4\+ years of experience in a predictive analytics or data analysis OR Advanced Degree (e.g., Master's, PhD) in mathematics, computer science, statistics, science and engineering, AI, or other similar quantitative discipline and 2\+ years of experience in predictive analytics or data analysis.
- 2\+ years of experience in training and validating statistical, physical, machine learning, and other advanced analytics models.
- 2\+ years of experience in Python for performing statical analysis and/or building and scoring AI/ML models
- Experience writing code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
- Experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
- Experience in performing ad\-hoc analytics using descriptive, diagnostic, and inferential statistics and understanding real\-world constraints such as latency, cost, and reliability in AI solution designs.
- Experience with the concepts and technologies associated with classical supervised modeling for prediction such as linear/logistic regression, discriminant analysis, support vector machines, decision trees, forest models, etc.
- Experience with the concepts and technologies associated with unsupervised modeling such as k\-means clustering, hierarchical/agglomerative clustering, neighbors algorithms, DBSCAN, etc.
- Experience communicating analytical and modeling results to non\-technical business partners with emphasis on business recommendations and actionable applications of results.
What sets you apart:
- Experience in LLMs and agentic systems development with frameworks such as LangChain/LangGraph, AgentCore, VertexAI, MCP, or others, with proven experience including prompt engineering, tuning and post\-training techniques, multi\-agent systems, agent optimization and tool use, RAG and context optimization, and observability and monitoring.
- MLOps Integration experience in facilitating engineering implementation of production scaled AI solutions in partnership with dedicated AI Engineers in cloud environments such as AWS or GCP.
- Ability to assess regulatory implications and expectations of distinct modeling efforts across risk stripes, including experience in the documentation and statistical validation of models for risk management.
- Financial services, insurance, banking, or other highly regulated industry experience.
- Experience with cloud\-native application development and modernization initiatives.
- Experience mentoring junior developers.
- US military experience through military service or a military spouse/domestic partner
Compensation range: The salary range for this position is: $114,080 \- $218,030\.
USAA does not provide visa sponsorship for this role. Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H\-1B, TN, STEM OPT Training Plans, etc.).
Compensation: USAA has an effective process for assessing market data and establishing ranges to ensure we remain competitive. You are paid within the salary range based on your experience and market data of the position. The actual salary for this role may vary by location.
Employees may be eligible for pay incentives based on overall corporate and individual performance and at the discretion of the USAA Board of Directors.
The above description reflects the details considered necessary to describe the principal functions of the job and should not be construed as a detailed description of all the work requirements that may be performed in the job.
Benefits: At USAA our employees enjoy best\-in\-class benefits to support their physical, financial, and emotional wellness. These benefits include comprehensive medical, dental and vision plans, 401(k), pension, life insurance, parental benefits, adoption assistance, paid time off program with paid holidays plus 16 paid volunteer hours, and various wellness programs. Additionally, our career path planning and continuing education assists employees with their professional goals.
For more details on our outstanding benefits, visit our benefits page on USAAjobs.com.
*Applications for this position are accepted on an ongoing basis, this posting will remain open until the position is filled. Thus, interested candidates are encouraged to apply the same day they view this posting.*
*USAA is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran.*
Salary Context
This $114K-$218K range is above the median for Data Scientist roles in our dataset (median: $160K across 258 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 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At USAA, 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 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($166K) sits 14% below the category median. Disclosed range: $114K to $218K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
USAA AI Hiring
USAA has 7 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in San Antonio, TX, US. Compensation range: $197K - $273K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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