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The University of Miami/UHealth Department of Informatics and Health Data Science has an exciting opportunity for a full time Manager, Data Science to work in Miami, FL.
The Manager, Data Science supports the Department Chair in managing and coordinating data science initiatives across the Department of Informatics and Health Data Science (DIHDS) and the Media and Innovation Laboratory (The MIL). The Manager applies advanced analytics, artificial intelligence, machine learning, predictive modeling, and related data-driven technologies to support the department’s strategic priorities across Research, Education, Clinical Innovation, Service/Outreach/Policy/Ethics, and Venture.
A major departmental priority is the development and implementation of large-scale, multidisciplinary research initiatives supported by the National Institutes of Health (NIH), the Advanced Research Projects Agency for Health (ARPA-H), and other federal, foundation, and industry sponsors. The Manager works closely with the Chair, faculty, investigators, engineers, technical teams, and collaborators across the University of Miami to address the data architecture, integration, analytics, modeling, infrastructure, and reporting requirements of complex, multi investigator, and multi-institutional projects.
The Manager also supports key departmental engineering and innovation initiatives, including the continued development, implementation, evaluation, and advancement of the MIL Agentic Data Scientist (MILADS) and other AI-enabled research tools. In collaboration with departmental leadership and internal and external partners, the Manager contributes technical expertise to the development, validation, translation, and potential commercialization of innovative products and technologies.
Core responsibilities:
- Conducts initial assessments and data analyses to identify and quantify/qualify recommended opportunities for improvement.
- Serves as a business change agent leading process re-engineering activities/project.
- Manages the daily activities of the team responsible for identifying business trends and problems through big data analysis.
- Oversees the interpretation of results using a variety of techniques, ranging from simple data aggregation via statistical analysis to complex data mining.
- Manages the design and implementation of big data solutions for the organization utilizing big data tools.
- Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex high-volume data from a variety of sources.
- Develops infrastructure systems that connect internal data sets.
- Manages analysis of large quantities of data and presents insights and predictions to support management planning, execution and monitoring of business decisions.
- Manages team, budget, and project staffing assignments.
- Establishes and continuously assesses the effectiveness of the internal controls within the unit and compliance with University policies and procedures.
- Ensures employees are trained on controls within the function and on University policy and procedures.
Department Specific Functions:
- Manages and coordinates the department’s data science portfolio in alignment with the strategic priorities of DIHDS, The MIL, the Miller School of Medicine, and the University of Miami.
- Works closely with the Department Chair, faculty, investigators, engineers, and research teams to translate scientific, operational, and population-health questions into defined data science projects, analytical plans, technical requirements, and measurable outcomes.
- Supports the development of large-scale NIH, ARPA-H, and other externally sponsored proposals by contributing to study design, data strategies, preliminary analyses, technical plans, analytic workflows, infrastructure requirements, budgets, and related application components.
- Provides ongoing data science and analytical support for funded studies, including data acquisition, ingestion, cleaning, harmonization, integration, documentation, analysis, visualization, and reporting.
- Manages data science personnel, analysts, consultants, and other technical contributors assigned to departmental research, education, engineering, and innovation initiatives.
- Coordinates the development and application of advanced analytical methods, including statistical analysis, predictive modeling, machine learning, classification, time-series analysis, causal inference, feature engineering, and model-performance evaluation.
- Supports the design, development, implementation, evaluation, and continued advancement of MILADS and related artificial intelligence and agentic-AI tools that accelerate data ingestion, analysis, interpretation, reporting, and research translation.
- Develops and maintains scalable and reproducible data science workflows, analytical pipelines, reusable code, data dictionaries, technical documentation, dashboards, visualization tools, and reporting systems.
- Establishes and maintains departmental practices for evaluating the accuracy, reliability, fairness, reproducibility, transparency, interpretability, privacy, security, and practical value of statistical models and AIenabled solutions.
- Collaborates with University information technology, data-governance, cybersecurity, privacy, compliance, and research-administration teams to ensure that departmental data science activities comply with institutional policies, sponsor requirements, and applicable regulations.
- Provides data science support for research protocols, progress reports, manuscripts, conference presentations, technical reports, demonstrations, and other scholarly or professional products.
- Supports the department’s education and workforce-development activities by developing workshops, guided working sessions, technical resources, and mentoring opportunities that strengthen data literacy, computational skills, and the responsible use of artificial intelligence.
- Coordinates data science contributions to departmental programs such as MILADS, NHLBI-funded PRIDE, biomedical informatics, digital health, precision medicine, and other research and educational initiatives.
- Supports key engineering initiatives by coordinating data requirements, analytical methods, model development, technical validation, documentation, and performance evaluation across multidisciplinary project teams.
- Supports the department’s Venture vertical by contributing data science expertise to the design, development, testing, validation, and translation of innovative products, platforms, prototypes, and technologies.
- Collaborates with University technology-transfer resources and external partners to support product commercialization, intellectual-property development, industry engagement, and the transition of research innovations into scalable solutions.
- Develops and maintains collaborations with academic units, healthcare organizations, technology companies, community partners, government agencies, and other external stakeholders to expand access to data, expertise, technologies, funding, and translational opportunities.
- Evaluates emerging data science, machine-learning, generative-AI, and agentic-AI technologies and recommends appropriate tools, platforms, methodologies, and investments for departmental consideration.
- Establishes performance indicators and evaluation frameworks to measure the utilization, effectiveness, scientific contribution, operational value, scalability, and impact of departmental data science initiatives.
- Performs other duties consistent with the scope, responsibilities, qualifications, and strategic priorities of the position.
This list of duties and responsibilities is not intended to be all-inclusive and may be expanded to include other duties or responsibilities as necessary.
Core Qualifications:
- Bachelor’s degree in relevant field required.
- Minimum 3 years of relevant experience required.
- Operational Management: Optimizes day-to-day operations and processes for efficiency and effectiveness.
- Organizational Development: Ability to implement strategies to improve organizational effectiveness, engagement, and manage change.
- Financial Oversight: Knowledge of financial operations and management.
- Team Leadership: Ability to create and maintain a cohesive and productive team environment, build positive working relationships and work collaboratively with others.
- Technical Proficiency: Skilled in using office software, technology, and relevant computer applications.
- Communication: Strong verbal and written communication skills to convey ideas clearly and persuasively.
Any relevant education, certifications and/or work experience may be considered.
The University of Miami offers competitive salaries and a comprehensive benefits package including medical, dental, tuition remission and more.
UHealth-University of Miami Health System, South Florida's only university-based health system, provides leading-edge patient care powered by the ground breaking research and medical education at the Miller School of Medicine. As an academic medical center, we are proud to serve South Florida, Latin America and the Caribbean. Our physicians represent more than 100 specialties and sub-specialties, and have more than one million patient encounters each year. Our tradition of excellence has earned worldwide recognition for outstanding teaching, research and patient care. We're the challenge you've been looking for.
The University of Miami is an Equal Opportunity Employer. Applicants and employees are protected from discrimination based on certain categories protected by Federal law.
Job Status:
Full time Employee Type:
Staff
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At University of Miami, 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 in Demand for This Role
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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
University of Miami AI Hiring
University of Miami has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Coral Gables, FL, US, Miami, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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