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About This Role
Job ID
R0119640
Date posted
08/05/2026
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
The role we are seeking a Lead Business Strategy Analyst adept at GenAI Strategy \& Portfolio Management, with the ability to identify, prioritize, and govern GenAI use cases that align with business objectives, enterprise standards, and deliver measurable value. A strong Technical \& Analytical Acumen is essential, encompassing a deep understanding of GenAI concepts, data and analytics architectures, AI solution design, and performance measurement to effectively guide both technical teams and business stakeholders. Crucially, this position demands exceptional Cross\-Functional Leadership \& Influence to drive alignment across business, analytics, technology, and operations teams, establish necessary standards, and foster consistent execution of GenAI initiatives, even without direct authority.
The candidate will be instrumental in Value Realization \& Decision Support, possessing expertise in defining success metrics, developing business cases, measuring outcomes, and providing data\-driven recommendations to maximize GenAI adoption and business impact. Finally, a robust understanding of Governance \& Operationalization is required to establish repeatable frameworks, best practices, and oversight processes that ensure GenAI solutions are scalable, compliant, and consistently deployed throughout the organization.
This role is remote eligible in the continental U.S. with occasional business travel. However, individuals residing within a 60\-mile radius of a USAA office will be expected to work on\-site four days per week.
What you'll do:
- Leads discussions with key stakeholders to communicate information learned from analyses, provide input into line of business strategy development, and drive and influence business decisions. Leads integration of the analytic strategy and business strategy.
- Oversees efforts to identify key business assumptions and hypotheses around line of business strategy. Continuously refines hypotheses and identifies business questions to explore further.
- Develops the analytical framework and blueprint to answer business questions identified in the business portfolio, product, or member experience and provides support to lower levels towards this effort.
- Collaborates with key stakeholders to evaluate and uncover complex or critical strategic insights related to Profit \& Loss performance including Product Strategy, Pricing, Marketing, Sales, Credit Risk, Distribution Channels, and Member Experience.
- Applies expert analytical rigor to define outcome measures, improve prioritization, increase agility in decisioning, improve ability to evaluate progress towards business outcomes, and to evaluate risks to strategic goals.
- Effectively influences and drives strategic agreement utilizing subject matter expertise and interpersonal and negotiation skills.
- Serves as a team lead and provides guidance and on\-the\-job training to team members.
- Ensures 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 Business, Science, Finance, Economics or related field; OR 4 years of relevant education and/or experience.
- 8 years of data and/or analytics or strategy consulting experience; OR a minimum of 6 years of data and/or analytics or strategy consulting experience and up to 2 years of progressive functional business relevant experience for a total of 8 years of combined experience; OR Advanced Degree in Business, Science, Finance, Economics or related discipline and 6 years of experience in data and/or analytics or strategy consulting.
- Experience identifying business needs and developing strategic plans driven by qualitative/quantitative analysis and market insights.
- Experience working with leadership teams to identify key opportunities to develop and enhance business strategy using quantitative and qualitative analytics.
- Experience influencing business decisions.
- Strong analytical skills with experience using hypotheses\-driven problem solving.
- Extensive experience leading and performing complex data analysis using various data analytics tools (i.e.Microsoft Excel, Tableau, R, Python, SQL, Snowflake, SAS, Adobe Analytics).
What sets you apart:
- GenAI Strategy \& Portfolio Management – Ability to identify, prioritize, and govern GenAI use cases, ensuring alignment to business objectives, enterprise standards, and measurable value outcomes.
- Technical \& Analytical Acumen – Strong understanding of GenAI concepts, data and analytics architectures, AI solution design, and performance measurement to effectively challenge, influence, and guide technical teams and business stakeholders.
- Cross\-Functional Leadership \& Influence – Ability to drive alignment across business, analytics, technology, and operations teams; establish standards; and foster consistent execution of GenAI initiatives without direct authority.
- Value Realization \& Decision Support – Expertise in defining success metrics, developing business cases, measuring outcomes, and providing data\-driven recommendations that maximize GenAI adoption and business impact.
- Governance \& Operationalization – Ability to establish repeatable frameworks, best practices, and oversight processes that ensure GenAI solutions are scalable, compliant, and consistently deployed across the organization.
- US military experience gained through military service or gained as a military spouse / domestic partner
Compensation range: The salary range for this position is: $127,310 \- $243,340.
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 $127K-$243K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At USAA, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($185K) sits 14% below the category median. Disclosed range: $127K to $243K.
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 AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
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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