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Overview
At TDS, we're investing in the future of artificial intelligence, data, and analytics to transform how we serve customers, operate our business, and compete in a rapidly evolving digital landscape. We are seeking a visionary and results-oriented Vice President - AI, Data & Analytics to lead this enterprise-wide transformation.
Reporting directly to the Chief Information Officer, this executive leader will define and execute TDS's strategy for artificial intelligence, data, analytics, business intelligence, and data governance. You will partner with leaders across the organization to identify high-value opportunities, build modern data and AI capabilities, and deliver solutions that create measurable business impact.
This is a unique opportunity to build and scale enterprise AI capabilities from strategy through execution while leading talented teams across AI, data engineering, analytics, business intelligence, and data science.
This position has the flexibility to work remotely from a home office in any of the 40+ states where TDS operates. Travel of up to 25% may be required based on business needs and the selected candidate's proximity to TDS key office locations in Chicago, IL and Madison, WI. Responsibilities
AI, Data & Analytics Strategy Partners with the CIO to develop and execute the enterprise AI, data, analytics, and governance strategy, including systems, platforms, roadmaps, investments, and operating models that enable secure, trusted, and business-aligned outcomes.
Business Partnership Partner with other business leaders to identify, prioritize, and deliver high-value AI and data solutions that improve customer experience, operational efficiency, financial performance, and strategic outcomes.
Delivery and Adoption Lead the acquisition, development and enterprise-wide adoption of AI, data management and business analytics while ensuring responsible governance, privacy, security, compliance, and ethical use in collaboration with technology, legal, compliance, audit, and risk stakeholders.
Training, Execution & Value Realization Enable enterprise AI and data capabilities, drive adoption through training and best practices, and measure business impact through governance, portfolio management, and value realization frameworks.
Vendor Strategy and Management Oversee vendor strategy, selection, performance, and partnerships for AI, data, analytics, and related technology platforms.
Talent Leadership Build and lead a high-performing team across AI, data, and analytics functions, fostering a culture of accountability, innovation, collaboration, and continuous learning. Qualifications
Required Qualifications:
- Bachelors degree (or higher) -OR- 4+ years professional work experience.
- 15+ years' experience of progressive technology, data, analytics, AI, or digital leadership experience, including significant experience leading enterprise-scale AI, data, and analytics functions.
Other Qualifications:
- Master's Degree preferred in Computer Science, Data Science, Analytics, Engineering, Business Administration, or a related field.
- Significant experience developing and executing AI, data, and analytics strategies, with a strong preference for relevant experience within telecommunications or digital infrastructure industries.
- Proven success leading large, cross-functional teams and building high-performing AI, analytics, data, or center of excellence organizations.
- Deep expertise in modern AI, machine learning, generative AI, data management, business intelligence, analytics, data governance, and cloud-based data platforms.
- Experience modernizing data environments and implementing scalable, self-service, cloud-based analytics capabilities.
- Strong knowledge of modern technology delivery, governance, and operating models, including Agile, product management, DataOps/MLOps, security, privacy, compliance, and enterprise architecture.
- Proven ability to influence senior leaders to translate business needs into scalable technology solutions and measurable outcomes.
- Experience managing strategic vendors and technology partners.
- Exceptional executive communication, influence, and stakeholder management skills.
Benefits We believe in taking care of our team, which is why we offer comprehensive benefits to support your health, financial well-being, and overall happiness. Join us and experience a work environment where your well-being is a top priority!
Associates scheduled to work 20 or more hours per week have access to medical, dental, vision, life insurance, a 401(k) plan, education assistance, and paid time off benefits such as vacation (15 days accrued per year for full-time; pro-rated for associates working between 20-39 hours), sick leave (10 days accrued per year for full-time and pro-rated for all other associates), seven paid national holidays and one floating holiday, paid parental leave (6 weeks after 12 months of employment), among others. Short Term Disability and Long Term Disability coverage is also available for associates working 30 or more hours per week. Associates scheduled to work under twenty hours per week or for a limited term (i.e. interns) are eligible for medical coverage, the 401(k) plan (if 18 or older), as well as sick leave (10 days accrued per year for full-time and pro-rated for all other associates).
In addition to these benefits, all associates will have the opportunity to participate in our Associate Resource Groups, which are designed to encourage community and facilitate professional development. To learn more, click here.
Who is TDS? Telephone and Data Systems, Inc. (TDS) provides broadband, video and voice through its TDS Telecom business. Its Array business leases and offers tower space to third-party carriers. Founded in 1969, TDS is headquartered in Chicago.
At TDS, we are committed to Equal Employment Opportunity (EEO) and value the difference of backgrounds, experiences and perspectives in our workforce. We consider all qualified applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other status protected by law.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At TDS, 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 $211,950 based on 13,048 positions with disclosed compensation.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
TDS AI Hiring
TDS has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Madison, WI, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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