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About This Role
Penn State Health \- Penn State Health Corporation
Location: US:PA: Hershey
Work Type: Full Time
FTE: 1\.00
Shift: Day
Hours: 8 hours
SUMMARY OF POSITION:
The Senior Director of Data Governance, Management, and Artificial Intelligence is a pivotal enterprise leadership role within Penn State Health's Information Services department. This leadership position is responsible for developing and operationalizing a comprehensive strategy that spans three integrated disciplines: data governance, enterprise data management, and artificial intelligence. The Senior Director will drive a culture of data stewardship across the health system, ensuring that clinical, operational, and financial data assets are accurate, accessible, secure, and aligned with regulatory obligations including HIPAA and HITECH.
Working closely with clinical, operational, legal, compliance, and technology leaders, this role champions responsible Artificial Intelligence (AI) adoption—from model governance and ethical use frameworks to real\-world clinical decision\-support implementation. The Senior Director will build and lead a high\-performing team, manage governance committees, oversee enterprise data platforms and data quality programs, and serve as the health system's voice on data\-driven innovation. This position reports directly to the VP/Chief Application Officer of Information Services.
Responsibilities
Data Governance Strategy \& Execution Develop, maintain, and execute a comprehensive enterprise data governance framework aligned with Penn State Health's strategic goals, mission, and regulatory requirements.
- Establish and lead Data Governance and AI Governance Committees and sub\-committees, including defining charters, membership, decision rights, and escalation pathways.
- Define and enforce data policies, standards, definitions, and lineage protocols across all clinical and administrative data domains (clinical, claims, financial, workforce, research).
- Ensure audit\-readiness through continuous documentation, controls monitoring, and evidence management for internal audits and regulatory reviews.
- Develop KPIs and dashboards that provide real\-time visibility into data health, governance maturity, and policy compliance across the enterprise.
- Partner with Legal, Compliance, Privacy, and Security teams to manage data rights, usage controls, and consent frameworks governing patient and workforce data.
Enterprise Data Management
- Lead end\-to\-end enterprise data management including data architecture, data quality, master data management (MDM), metadata management, and data cataloging.
- Collaborate with Systems IS Application Directors for integration of core healthcare data domains—EHR (Epic), claims, revenue cycle, enterprise resource management, imaging, and research—into centralized enterprise data platforms and data warehouses.
- Drive cloud data platform strategy, including data ingestion pipelines, Extract Transform Load and Extract Load Transform (ETL/ELT) processes, and analytics enablement using modern data stack technologies.
- Establish data quality standards and stewardship models; implement automated monitoring and remediation workflows to maintain data integrity across source systems.
- Define and implement data lifecycle management policies covering data retention, archival, and disposition in compliance with healthcare regulatory standards. •
- Collaborate with IS, Epic implementation teams, and clinical and finance operations to ensure data consistency and interoperability across the health system's application ecosystem.
Artificial Intelligence Strategy \& Governance
- Lead the health system's AI governance framework, including model registration, risk tiering, bias evaluation, and ongoing model performance monitoring.
- Partner with clinical, operational, and research stakeholders to evaluate, pilot, and scale AI and Machine Learning (ML) use cases that improve patient outcomes, operational efficiency, and financial performance.
- Establish transparent AI deployment protocols ensuring compliance with emerging federal and state AI regulations as well as accreditation standards.
- Oversee responsible AI practices including fairness assessments, transparency requirements, and review processes for clinical decision\-support applications.
- Build and maintain an AI model inventory with associated documentation of validation results, training data provenance, intended use, and monitoring schedules.
- Champion AI literacy and change management initiatives across clinical and administrative teams to accelerate adoption and informed use of data\-driven tools.
Leadership \& Team Management
- Build, develop, and retain a high\-performing team of data governance professionals, data stewards, data engineers, and analysts.
- Establish clear performance expectations, foster a culture of accountability, innovation, and continuous improvement within the team.
- Mentor and develop emerging talent; create career pathways and professional development opportunities aligned with evolving healthcare data and AI disciplines.
- Lead cross\-functional working groups and collaborative initiatives with IS application and technology teams, research computing, and enterprise analytics.
Represent IS Data \&AI Governance in leadership briefings and external stakeholder engagements as a subject matter expert
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MINIMUM QUALIFICATION(S):
- Bachelor's degree in Computer Science, Data Science, Information Systems, Healthcare Administration or a related field required.
- Minimum ten (10\) years of progressive experience in data governance, data management, or healthcare informatics, with at least 7 (seven) years in a senior leadership role.
- Demonstrated experience developing and operationalizing enterprise data governance frameworks within a complex healthcare or health system environment.
- Proven track record leading AI/ML strategy, governance programs, or data science functions, including responsible AI practices in clinical or regulated settings.
- Experience working with Epic EHR or equivalent large\-scale clinical information systems in a governance, analytics, or data integration capacity.
- Hands\-on experience with enterprise data platforms, cloud data warehousing (Azure Synapse, Databricks, Snowflake, or equivalent), and modern data stack tooling.
- Track record of successfully building, leading, and developing high\-performing cross\-functional teams of 10\+ staff.
PREFERRED QUALIFICATION(S):
- Master's degree in Business Administration (MBA), Data Science, Healthcare Administration or a related discipline are strongly preferred.
- Relevant certifications preferred: CDMP (Certified Data Management Professional), CIPP/US, CIPT, CHDA or equivalent AI/data governance credentials
WHY PENN STATE HEALTH?
Penn State Health offers exceptional opportunities to learn and grow, exposure to a wide patient population, and the ability to provide individualized, innovative, and specialized care to patients in the community.
Penn State Health offers an exceptional benefits package including medical, dental and vision with no waiting period as well as a Total Rewards Program that highlights a few of the many additional offerings below:
- *Be Well* with Employee Wellness Programs, and Fitness Discounts (University Fitness Center, Peloton).
- *Be Balanced* with Generous Paid Time Off, Personal Time, and Paid Parental Leave.
- *Be Secured* with Retirement, Extended Illness Bank, Life Insurance, and Identity Theft Protection.
- *Be Rewarded* with Competitive Pay, Tuition Reimbursement, and PAWS UP employee recognition program.
- *Be Supported* by the HR Solution Center, Learning and Organizational Development and Virtual Benefits Orientation, Employee Exclusive Concierge Service for scheduling.
WHY PENN STATE HEALTH CORPORATION?
There are many ways to make an impact with one of the leading research, teaching, and clinical healthcare systems in the country. Through a combination of operational, corporate, clinical, and nonclinical roles, we are advancing excellence and innovation in health care together as one team. As Penn State Health continues to evolve for the future, we are committed to hiring dedicated employees who are passionate about delivering the best possible support across our entire integrated health system.
Within Penn State Health’s Shared Services Entity, we encourage our employees at every turn to continue their education and advancement. Numerous opportunities are available for professional development and career growth.
YOU TAKE CARE OF THEM. WE’LL TAKE CARE OF YOU. State\-of\-the\-art equipment, endless learning, and a culture of excellence – that’s Penn State Health. But what makes our healthcare award\-winning? That’s all you.
*This job posting is a general outline of duties performed and is not to be misconstrued as encompassing all duties performed within the position. Eligibility for shift differential pay based on the terms outlined in company policy or union contract.* *All individuals (including current employees) selected for a position will undergo a background check appropriate for the position's responsibilities.*
*Penn State Health is an Equal Opportunity Employer and does not discriminate on the basis of any protected class including disability or veteran status. Penn State Health’s policies and objectives are in direct compliance with all federal and state constitutional provisions, laws, regulations, guidelines, and executive orders that prohibit or outlaw discrimination.*
Union: Non Bargained
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 Penn State Health, 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. Director-level AI roles across all categories have a median of $274,554.
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.
Penn State Health AI Hiring
Penn State Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hershey, PA, 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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