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About This Role
Job Description
The Senior Analyst\-Data Science (Quality Analytics) supports the Office of the Chief Medical Officer (CMO) and the Department of Quality, Patient Safety, \& Accreditation by performing advanced healthcare analytics, developing scalable reporting solutions, and translating complex clinical and operational data into actionable insights. This role independently manages moderately complex analytics projects and serves as an experienced analytics resource for quality improvement initiatives across the clinical enterprise. The Senior Analyst leverages advanced analytical methods, healthcare quality knowledge, and data visualization tools to support performance improvement, regulatory reporting, patient safety initiatives, and strategic operational decision\-making.
Responsibilities
1\. Independently perform advanced quantitative and statistical analyses to support healthcare quality, patient safety, infection prevention, operational improvement, and regulatory initiatives.
2\. Collaborate with Quality Department specialists, clinical, and operational teams to define metrics, identify performance gaps, and develop data\-driven improvement strategies.
3\. Analyze large and complex datasets to identify trends, variation, risks, and opportunities for improving patient outcomes, reducing harm, improving efficiency, and lowering costs.
4\. Design, develop, automate, and maintain dashboards, scorecards, and reports using Power BI, SQL, Excel, and other analytics platforms.
5\. Develop and validate data extraction queries, reporting logic, and data models to ensure accuracy, consistency, and reliability of analytics deliverables.
6\. Utilize advanced statistical methods, including regression analysis, SPC charting, forecasting, and trend analysis, to support evidence\-based decision\-making.
7\. Support quality and regulatory reporting requirements related to CMS, NHSN, Joint Commission, Leapfrog, Vizient, HEDIS, and other benchmarking programs.
8\. Translate complex analytical findings into actionable insights and concise summaries for both technical and non\-technical stakeholders.
9\. Participate in and support root cause analyses, performance improvement initiatives, and deep\-dive investigations related to patient safety events, hospital\-acquired conditions, mortality, readmissions, and operational performance.
10\. Integrate and validate data from multiple internal and external sources, including EMR systems, quality databases, patient safety systems, and financial or operational systems.
11\. Identify opportunities for process improvement, reporting automation, and data standardization to improve efficiency and reduce manual work.
12\. Collaborate with interdisciplinary teams and external partners to acquire, validate, and interpret healthcare data.
13\. Assist in developing standardized definitions, methodologies, and documentation for quality metrics and reporting practices.
14\. Provide guidance and informal mentorship to less senior analysts and operational stakeholders regarding reporting tools, dashboards, and interpretation of data.
15\. Present findings, visualizations, and recommendations to departmental leaders, committees, and project teams.
16\. Maintain current knowledge of healthcare analytics methodologies, quality improvement science, regulatory requirements, healthcare technology, and emerging industry trends.
17\. Perform other related duties as assigned.
Qualifications
- Knowledge: Ability to demonstrate in\-depth knowledge of concepts, practices and policies with the ability to use them in complex varied situations; able to independently frame ambiguous problems and recommend analytic approaches.
- Programming/Systems: Programming experience preferably SQL and Python in a Healthcare system setting. Knowledge of Epic System (other Electronic Health Records) and Statistical Software (SAS, R) is preferred. Familiarity with version control (Git) and basic MLOps practices is a plus.
- Project Management: Ability to coordinate the diverse components of projects through quality project planning, execution, stakeholder management, and change control to achieve the required balance of time, cost and quality.
- Communication: Ability to communicate understanding in ways that capture interest, inform, and gain support; explain technical concepts to peers and leaders; ability to read and interpret professional journals, financial reports, and legal documents; write reports, business correspondence, and procedural manuals.
- Teamwork: Builds and maintains positive working relationships within work groups and across departments through open communication and collaboration; works with others to accomplish goals and objectives; maintains a culture of collaboration and professionalism.
Education:
- Bachelor's degree in a quantitative analytics field such as Economics, Finance, Statistics, Mathematics, Actuarial Sciences, Operations Research, Data and/or Business Analysis, Data Science or other quantitative discipline.
Required Skills
Three (3\) years of related data/analytics work experience required or Two (2\) years of related work experience with an advanced degree (Master's or PhD) in Business, Science, Finance, Mathematics, Economics, Data Science, or related discip
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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 UT Health Science Center at San Antonio, 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.
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.
UT Health Science Center at San Antonio AI Hiring
UT Health Science Center at San Antonio has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Antonio, TX, US.
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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