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GENERAL PURPOSE AND SCOPE OF POSITION:
Under the direction of the SVP & Chief Information Officer, the Assistant Vice President of Data, Analytics, and AI Strategy provides enterprise leadership for Ivy Tech’s data, analytics, and artificial intelligence priorities. This position advances a College-wide strategy for trusted data, actionable insights, and responsible AI that strengthens decision-making, improves student and employee experiences, and supports institutional priorities. The AVP partners with senior leaders, chancellors, academic and administrative units, and technology teams to align data and AI initiatives with College goals, strengthen governance and data quality, and identify innovative opportunities to create measurable value. The role is responsible for building future-ready data and AI capabilities that are secure, scalable, sustainable, and practical for leaders across the College to use.MAJOR RESPONSIBILITIES:
- Lead the College’s enterprise data, analytics, and AI strategy in alignment with institutional priorities, student success goals, operational needs, and long-term digital transformation efforts.
- Strengthen data governance, data quality, privacy, security, and responsible AI practices so leaders can rely on trusted information and use emerging technologies appropriately.
- Oversee enterprise reporting, dashboards, visual analytics, advanced analytics, and AI-enabled insights that are clear, actionable, and connected to institutional outcomes.
- Serve as a strategic advisor to executives, chancellors, and functional leaders by translating data, analytics, and AI opportunities into practical recommendations and decisions.
- Identify and advance innovative uses of artificial intelligence, predictive analytics, automation, and modern data platforms to improve planning, service delivery, institutional effectiveness, and student outcomes.
- Partner across academic, administrative, institutional research, and technology areas to prioritize high-value initiatives, define success measures, evaluate outcomes, and ensure solutions are scalable and sustainable.
- Lead, mentor, and develop data, analytics, institutional research, and business intelligence teams to foster collaboration, innovation, accountability, and continuous improvement.
- Collaborate with technology leaders to ensure data architecture, integrations, platforms, and tools support secure, modern, and flexible use of data and AI at scale.
- Prepare executive-level materials, recommendations, and Board-ready insights that connect data and AI work to institutional priorities, risks, opportunities, and measurable outcomes.
- Champion a culture of data-informed decision-making and responsible innovation by helping leaders understand, trust, and apply data and AI insights in practical ways.
SUPERVISION RECEIVED: Senior Vice President & Chief Information Officer
SUPERVISION GIVEN: Director of Institutional Research, Director of Survey Research, and Executive Director of Business Intelligence
HIRING RANGE: $160,000 - $170,000
EDUCATION:
A bachelor's degree in business administration, computer science, data science, information systems, statistics, mathematics, economics, or a related field is required. A master's degree in a related field is preferred.
EXPERIENCE:
- Seven or more years of progressively responsible leadership experience in data strategy, analytics, institutional research, business intelligence, AI strategy, or related fields is required.
- Demonstrated ability to lead enterprise data and analytics teams, programs, or initiatives that improve decision-making, strengthen institutional outcomes, and deliver measurable value.
- Strong understanding of modern data platforms, analytics tools, reporting environments, data architecture, visualization, and emerging AI capabilities.
- Experience advancing data governance, data quality, privacy, security, ethical data use, and responsible AI practices in a complex organization.
- Ability to evaluate emerging technologies, challenge assumptions, and translate innovation into practical, scalable, and sustainable solutions.
- Strong executive presence and communication skills, including the ability to present complex technical concepts, risks, tradeoffs, and recommendations clearly to executive, academic, administrative, and non-technical audiences.
- Demonstrated strategic thinking, intellectual curiosity, sound judgment, and comfort working through ambiguity in a fast-changing data and AI landscape.
- Must demonstrate the ability to promote an inclusive environment that reflects the broad diversity and backgrounds represented by our students and employees and in which every individual feels respected and valued.
Institutional Expectations
- Strategic thinking and sound judgment.
- Portfolio and project leadership.
- Collaborative influence and relationship building.
- Executive communication and decision facilitation.
- Planning, organization, and follow-through.
- Systems thinking and continuous improvement.
- Must consistently exercise independent judgment and discretion in matters of significance.
- Must demonstrate the ability to foster a respectful and inclusive environment in which students and employees feel valued and supported.
Ivy Tech is committed to supporting the well-being, growth, and financial security of our full-time faculty and staff. Our comprehensive benefits package includes:
Health & Wellness
- Multiple medical plan options paired with a Health Savings Account with a generous employer contribution
- Dental plan with no-cost preventive services and coverage for orthodontia
- Vision plan with low-cost exams and allowances for glasses or contact lenses
- Employee Assistance Program offering no-cost confidential counseling sessions, legal consultations, financial planning consultations, and other resources
- Wellness program with opportunities to earn $250 in Wellness Rewards
- Flexible Spending Accounts for healthcare (limited purpose) and dependent care
Retirement & Financial Security
- 10% employer retirement contribution, fully vested after two years
- Basic life insurance equal to annual salary paid by the College, with optional supplemental coverage
- Short-term and long-term disability benefits
Educational Benefits
- Fee remission for employees, spouses, and dependent children
- Tuition assistance for undergraduate, graduate, and doctoral programs
- Paid professional development opportunities
Work‑Life Balance
- Generous paid time off, including vacation, sick leave, holidays, and winter recess
- Flexible work arrangements where available
- Paid childbirth recovery leave (8 weeks)
- Paid parental leave (4 weeks)
Additional Perks
- Eligibility for Public Service Loan Forgiveness
- Additional discounts on gym memberships, transportation, and various retail services
Ivy Tech is proud to offer benefits that support your health, your family, and your future—because when our employees thrive, our students and communities thrive too. For more information on Ivy Tech Benefits, visit https://careers.ivytech.edu/benefits.
Note: Employees who re-hire with the College within 180 days of leaving a full-time position with the College may be eligible for additional benefits depending on their bridged seniority date.
Ivy Tech Community College is an accredited, equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, ethnicity, national origin, marital status, religion, sex, gender, sexual orientation, gender identity, disability, age or veteran status. As required by Title IX of the Education Amendments of 1972, Ivy Tech Community College does not discriminate on the basis of sex, including sexual harassment in its educational programs and activities, including employment and admissions. Questions specific to Title IX may be referred to the College’s Title IX Coordinator or to the US Department of Education Office of Civil Rights.
Salary Context
This $160K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Ivy Tech Community College, 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 11,866 positions with disclosed compensation. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $160K to $170K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Ivy Tech Community College AI Hiring
Ivy Tech Community College has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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