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POSITION SUMMARY: The Director of Data Office \& AI Readiness will build and lead an enterprise Data Office that establishes trusted, durable, and business\-ready data across priority initiatives and core domains. The incumbent will be both strategic and execution\-oriented: they will drive results for priority programs while building the long\-term capabilities required for AI readiness at scale, data reuse, and quality. The Director of Data Office \& AI Readiness will stand up and scale an enterprise Data Office, collaborating closely with business stakeholders and technology partners to implement robust frameworks, data standards, and stewardship practices. The incumbent will manage enterprise data portfolio intake, prioritization, and sequencing of high\-value data initiatives, ensuring alignment to strategic business goals and maximizing value delivery.
PRINCIPAL RESPONSIBILITIES:
- Establishes and leads the Data Office as a formal enterprise capability with a clear charter, operating model, defined ownership and accountability, escalation paths, and measurable business outcomes.
- Defines roles, responsibilities, and engagement models across domain owners, stewards, and IT partners to support business\-led data accountability.
- Builds and leads the enterprise data operating model, including governance councils, working teams, and executive forums that prioritize data initiatives, resolve cross\-functional issues, inform, and ensure strategic alignment.
- Owns enterprise data demand and portfolio management, orchestrating intake, triage, prioritization, and sequencing the strategic roadmap of data initiatives based on business value, complexity, and reuse.
- Establishes and operationalizes the data ownership and stewardship framework, clarifying accountability for data elements, definitions, business rules, quality, systems of record, access, and usage across domains.
- Drives a value\-oriented data roadmap focused on high\-impact domains and strategic programs, delivering early governance wins and scaling successful practices across the organization to maximize business impact.
- Leads enterprise data quality and governance practices, including developing standard data definitions, implementing metadata and lineage management, establishing data certification processes, ongoing data quality monitoring, and issue management.
- Collaborates with enterprise architecture and data engineering teams to align modern data platforms and architecture with business needs; ensuring consistent data structures, reusable data assets, and effective lifecycle management.
- Enables AI readiness and self\-service analytics by collaboratively defining data management standards, privacy and security guardrails, and consumption models to support analytics, ML and AI initiatives.
- Champions enterprise\-wide adoption and cultural change by driving communication, training, stakeholder education and engagement; developing a high\-performing team, and fostering a data\-driven culture.
- Performs other job\-related duties as assigned or apparent.
KNOWLEDGE / SKILLS / ABILITIES:
- Deep expertise in enterprise data governance, data management principles, data ownership models, and designing business\-led data operating models
- Strong command of data quality management, metadata standards, data lineage, data certification, and data governance
- Proven ability to design and implement intake, triage, prioritization, and portfolio management processes for enterprise\-wide data initiatives
- Broad understanding of modern cloud\-based data architectures (e.g., multi\-layer or medallion architectures), data product management, and governed self\-service analytics environments
- Working knowledge of data catalog, data quality / observability, and MDM tools in modern cloud environments (e.g., Snowflake, data.world, AccelData, Reltio)
- Exceptional skill in translating complex data management and governance concepts into clear business terms for executive and non\-technical stakeholders
- Strategic mindset with a history of connecting data investments to tangible business value, risk mitigation, efficiency gains, and AI enablement
- Strong leadership and influence skills, including stakeholder management, conflict resolution, and change management within large, matrixed organizations
- Proven ability to develop and scale data practices, teams, and adoption across an enterprise, balancing strategic vision with pragmatic implementation.
- Comfort with ambiguity and a track record of establishing durable, repeatable processes and standards from the ground up.
QUALIFICATIONS:
- Demonstrated success in establishing, scaling, or significantly transforming a Data Office, Data Governance program, or similar enterprise data capability.
- Extensive experience designing governance operating models, data stewardship frameworks, decision\-rights structures, and executive governance forums in large, complex organizations.
- Track record of defining and operationalizing critical data elements (CDEs), data definitions, business rules, metadata and lineage management, data quality controls, certification processes, and issue management.
- Hands\-on experience developing effective intake, prioritization, and data portfolio management processes for large\-scale data initiatives tied to business strategy or transformation programs.
- History of partnering with architecture, engineering, security, privacy, legal, product, and business teams to define scalable data management practices, controls, and delivery standards.
- Experience with modern cloud\-based data platforms and governance technologies (e.g., enterprise data catalogs, metadata management and data quality/observability tools, MDM solutions); Snowflake and data.world experience strongly preferred.
- Demonstrated ability to enable advanced analytics and AI/ML initiatives through data management practices, ensuring data is prepared, high\-quality, and appropriately governed
- Exceptional executive communication and influence skills, with a proven ability to drive change and foster a data\-driven culture.
MINIMUM REQUIREMENTS:
- 10\+ years of progressive experience in enterprise data management, data governance, data strategy, analytics, or related data leadership roles.
- 7\+ years of experience leading cross\-functional teams, enterprise programs, or data transformation initiatives that span both business and technology functions.
*This position is a hybrid role based at Republic Service's headquarters in Phoenix, AZ. Hybrid is 4\-days per week onsite, and 1\-day remote. This position will not be eligible to be fully remote.*
Please note, our Director Data Office \& AI Readiness position is not sponsorship eligible.
Rewarding Compensation and Benefits
Eligible employees can elect to participate in:
- Comprehensive medical benefits coverage, dental plans and vision coverage.
- Health care and dependent care spending accounts.
- Short\- and long\-term disability.
- Life insurance and accidental death \& dismemberment insurance.
- Employee and Family Assistance Program (EAP).
- Employee discount programs.
- Retirement plan with a generous company match.
- Employee Stock Purchase Plan (ESPP).
- Paid Time Off (PTO)
- Benefits: https://jobs.republicservices.com/us/en/about\-us/benefits
*The statements used herein are intended to describe the general nature and level of the work being performed by an employee in this position, and are not intended to be construed as an exhaustive list of responsibilities, duties and skills required by an incumbent so classified. Furthermore, they do not establish a contract for employment and are subject to change at the discretion of the Company.*
EEO STATEMENT:Republic Services is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, disability, protected veteran status, relationship or association with a protected veteran (spouses or other family members), genetic information, or any other characteristic protected by applicable law. For any concerns relating to Republic Services’ commitment to equal opportunity employment, you may contact the AWARE Line at 1\-866\-3\-AWARE\-4\. ABOUT THE COMPANY
Republic Services, Inc. (NYSE: RSG) is a leader in the environmental services industry. We provide customers with the most complete set of products and services, including recycling, waste, special waste, hazardous waste and field services. Our industry\-leading commitments to advance circularity and support decarbonization are helping deliver on our vision to partner with customers to create a more sustainable world.
In 2025, Republic’s total company revenue was $16\.6 billion, and adjusted EBITDA was $5\.3 billion. We serve 13 million customers and operate more than 1,000 locations, including collection and transfer stations, recycling and polymer centers, treatment facilities, and landfills.
Although we operate across North America, the collection, recycling, treatment, or disposal of materials is a local business, and the dynamics and opportunities differ in each market we serve. By combining local operational management with standardized business practices, we drive greater operating efficiencies across the company while maintaining day\-to\-day operational decisions at the local level, closest to the customer.
Our customers, including small businesses, major corporations and municipalities, want a partner with the expertise and capabilities to effectively manage their multiple recycling and waste streams. They choose Republic Services because we are committed to exceeding their expectations and helping them achieve their sustainability goals. Our 42,000 team members understand that it's not just what we do that matters, but how we do it.
Our company values guide our daily actions:
- Safe: We protect the livelihoods of our colleagues and communities.
- Committed to Serve: We go above and beyond to exceed our customers’ expectations.
- Environmentally Responsible: We take action to improve our environment.
- Driven: We deliver results in the right way.
- Human\-Centered: We respect the dignity and unique potential of every person.
We are proud of our high employee engagement score of 86\. We have an inclusive and diverse culture where every voice counts. In addition, our team positively impacted 5\.1 million people in 2024 through the Republic Services Charitable Foundation and local community grants. These projects are designed to meet the specific needs of the communities we serve, with a focus on building sustainable neighborhoods.
STRATEGY
Republic Services’ strategy is designed to generate profitable growth. Through acquisitions and industry advancements, we safely and sustainably manage our customers’ multiple waste streams through a North American footprint of vertically integrated assets.
We focus on three areas of growth to meet the increasing needs of our customers: recycling and waste, environmental solutions and sustainability innovation.
With our integrated approach, strengthening our position in one area advances other areas of our business. For example, as we grow volume in recycling and waste, we collect additional material to bolster our circularity capabilities. And as we expand environmental solutions, we drive additional opportunities to provide these services to our existing recycling and waste customers.
Recycling and Waste
We continue to expand our recycling and waste business footprint throughout North America through organic growth and targeted acquisitions. The 13 million customers we serve and our more than 5 million pick\-ups per day provide us with a distinct advantage. We aggregate materials at scale, unlocking new opportunities for advanced recycling. In addition, we are cross\-selling new products and services to better meet our customers’ specific needs.
Environmental Solutions
Our comprehensive environmental solutions capabilities help customers safely manage their most technical waste streams. We are expanding both our capabilities and our geographic footprint. We see strong growth opportunities for our offerings, including PFAS remediation, an increasing customer need.
Sustainability Innovation
Republic’s recent innovations to advance circularity and decarbonization demonstrate our unique ability to leverage sustainability as a platform for growth.
The Republic Services Polymer Center is the nation’s first integrated plastics recycling facility. These innovative sites process rigid plastics from our recycling centers, producing recycled materials that promote true bottle\-to\-bottle circularity. We also formed Blue Polymers, a joint venture with Ravago, to develop facilities that will further process plastic material from our Polymer Centers to help meet the growing demand for sustainable packaging. We are building a network of Polymer Centers and Blue Polymer facilities across North America.
Our customers are increasingly looking for decarbonization solutions, and we are leveraging our network of landfills to meet that need. Republic is committed to harnessing landfill gas, a natural byproduct of decomposing waste, and converting it to energy. Republic has partnered with renewable gas developers to construct Renewable Natural Gas (RNG) plants at our landfills, expanding beyond the 77 projects we currently have to make progress towards our goal to beneficially reuse 50% more biogas by 2030 (2017 baseline year).
RECENT RECOGNITION
- Barron’s 100 Most Sustainable Companies
- CDP Discloser
- Dow Jones Best\-In\-Class Indices
- Ethisphere’s World’s Most Ethical Companies
- Fortune World’s Most Admired Companies
- Great Place to Work
- Sustainability Yearbook S\&P Global
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Republic Services, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Republic Services AI Hiring
Republic Services has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Phoenix, AZ, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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