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About This Role
The Director, Analytics, Insights & AI is a senior technology leader responsible for shaping and executing Rheem's enterprise data, analytics, and artificial intelligence strategy. Reporting to the Sr. Director of Platforms, Data, AI & Architecture, this leader will own the Enterprise Data Platform (EDP), advance Power BI adoption across the business, and drive AI/ML initiatives that deliver measurable operational and commercial value. The role requires equal parts strategic vision, technical depth, and the ability to build deep, trust-based relationships with business stakeholders across manufacturing, supply chain, finance, sales, and HR.
This individual will lead the Data Analytics & AI Center of Excellence (CoE), govern data quality and master data management (MDM), and orchestrate AI engineering pipelines using Azure AI Foundry and MLOps best practices. The Director will manage a team of approximately 8–10 analytics and AI professionals and will be accountable for budget, vendor relationships, and emerging technology evaluation within the analytics and AI domain.
This position will serve Rheem North America, and located in Atlanta, GA (hybrid). This role follows our hybrid work arrangement, with team members working in the office Monday through Thursday and remotely on Fridays.
- RESPONSIBILITIES
Enterprise Data, Analytics & AI Strategy
- Define and execute a multi-year enterprise data, analytics, and AI roadmap aligned to Rheem's business priorities across manufacturing, commercial, and support functions.
- Partner with the Sr. Director of Platforms, Data, AI & Architecture to integrate data strategy into the broader technology vision.
- Serve as a thought leader on emerging analytics, AI/ML, and generative AI trends within the industrial manufacturing and HVAC sector.
Enterprise Data Platform (EDP) Ownership
- Own end-to-end architecture, operations, and continuous improvement of the EDP built on Databricks and Microsoft Azure.
- Drive data lake and lakehouse design, data ingestion pipelines, and real-time and batch processing frameworks.
- Ensure platform scalability, security, and cost efficiency while meeting SLAs for enterprise reporting and analytics consumers.
Power BI Analytics & Self-Service BI
- Lead the enterprise Power BI program, establishing standards, governance, and a center of excellence model for self-service analytics.
- Drive adoption of Power BI across business units; partner with functional leaders to build decision-grade dashboards and KPI reporting.
- Establish certified datasets, report governance, and row-level security best practices.
AI/ML & Intelligent Automation
- Lead delivery of AI/ML initiatives including predictive analytics, demand forecasting, agentic AI, NLP, and intelligent automation solutions.
- Manage the AI engineering pipeline leveraging Azure AI Foundry, ensuring responsible AI practices, model governance, and MLOps.
- Identify and prioritize high-ROI AI use cases in partnership with business leaders; drive proof of concept through production deployment.
- Evaluate and integrate large language models (LLMs) and generative AI capabilities into enterprise workflows.
Data Analytics & AI Center of Excellence (CoE)
- Build, lead, and continuously mature the Data Analytics & AI CoE, including governance frameworks, best practices, reusable assets, and community of practice.
- Establish and enforce enterprise standards for data modeling, analytics development, and AI/ML model lifecycle management.
Business Stakeholder Collaboration & Influence
- Develop and sustain deep, trust-based relationships with senior business stakeholders across finance, supply chain, manufacturing operations, sales, and HR.
- Translate complex analytical and AI capabilities into business value narratives; influence C-suite and VP-level leaders.
- Act as the primary analytics business partner for key enterprise programs including Oracle Fusion ERP, Profisee MDM, and Microsoft technology platforms (Dynamics, myrheem, and other).
Data Governance, MDM & Data Quality
- Own the enterprise data governance strategy including policies, stewardship, lineage, and data quality frameworks.
- Lead integration and ongoing operations of Profisee MDM, ensuring master data accuracy across Oracle Fusion, Databricks, and downstream systems.
- Drive data quality KPIs and remediation workflows in partnership with business data stewards.
Team Leadership & Talent Development
- Recruit, develop, and retain a high-performing team of approximately 8–10 data engineers, analytics engineers, BI developers, and data scientists.
- Establish clear roles, career paths, and performance expectations; foster a culture of accountability, innovation, and continuous learning.
- Coach team members in agile delivery, data product thinking, and engineering excellence.
Budget, Vendor & Partnership Management
- Manage the analytics and AI domain budget, including licensing, cloud infrastructure costs, and professional services.
- Lead vendor relationships and contract negotiations for Databricks, Power BI, Azure AI services, Profisee, and other analytics tooling.
- Evaluate and onboard strategic technology partners to accelerate capability development.
Oracle Fusion & EBS Data Integration
- Ensure robust data integration between Oracle Fusion (and legacy Oracle EBS), the EDP, and downstream analytics and reporting systems.
- Migrate and centralize reporting and analytics through Fusion Data Intelligence (FDI)
- Partner with the Oracle Fusion program team to define analytics requirements and data architecture for Wave 1 and subsequent rollouts.
KPI Tracking, Security & Compliance
- Define and track KPIs for platform performance, data quality, BI adoption, and AI value realization.
- Ensure all data and AI systems comply with Rheem's information security policies, data privacy regulations, and audit requirements.
Emerging Technology Evaluation
- Continuously monitor and assess the analytics and AI technology landscape; present recommendations on emerging tools, platforms, and methodologies.
- Lead structured proof-of-concept engagements with new technology vendors and translate findings into actionable investment recommendations.
QUALIFICATIONS
- 12+ years of progressive experience in data, analytics, and/or AI roles, with at least 5+ years in a director-level leadership position.
- Proven track record leading enterprise data platform programs (data lake, lakehouse, or EDW) at scale in a manufacturing, industrial, HVAC, or building products environment.
- Deep technical expertise with Databricks, Azure Data Services (Data Factory, Synapse, Azure AI Foundry), Power BI, Python, and SQL.
- Demonstrated experience delivering AI/ML and generative AI solutions from concept through production, including MLOps practices.
- Strong experience with data governance, MDM platforms (e.g., Profisee, Informatica, or equivalent), and data quality management.
- Experience integrating data from Oracle ERP systems (Fusion or EBS) into enterprise data platforms.
- Exceptional stakeholder engagement skills , able to influence without authority and translate technical capabilities into business outcomes.
- High accountability and ownership mindset; proven ability to deliver in a fast-paced, matrixed organization.
- Experience managing and developing multi-disciplinary data and analytics teams.
- Strong budget management and vendor negotiation capabilities.
- Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, or related field; Master's degree preferred.
- Ability to travel up to 25% as required.
Preferred Qualifications:
- Experience with Oracle Fusion ERP data architecture and analytics integration in a manufacturing environment.
- Relevant certifications such as Databricks Certified Data Engineer/Associate, Microsoft Certified: Azure Data Engineer Associate, or DP-600 (Fabric Analytics Engineer).
- Experience building or maturing a Data Analytics & AI Center of Excellence (CoE).
- Familiarity with LeanIX enterprise architecture tooling or similar EA platforms.
- Exposure to global manufacturing operations spanning the US, Canada, and international markets.
- Experience with enterprise integration platforms.
- Demonstrated change management experience leading analytics and AI adoption programs.
- Background in decarbonization, sustainability analytics, or ESG reporting is a plus.
- Experience with agentic AI frameworks, prompt engineering, and responsible AI governance.
As a leader at Rheem, how you achieve results is as important as the results you achieve. While LEADING DEPARTMENTS AND FUNCTIONS, you will be expected to demonstrate the following competencies and behaviors:
- Building Business/Functional Talent
- Building Partnerships
- Coaching
- Compelling Communication
- Creating Influence
- Driving Change
- Driving Execution
- Driving Innovation
- Empowering Others
- Focusing on Customer-First
- Fostering Relationships/ Emotional Intelligence
- Promoting Global Business Growth
- Systematic/Tactical Direction
Equivalent combination of education, experience, and skills may supplement above minimum job requirements.
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 Rheem Manufacturing, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
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.
Rheem Manufacturing AI Hiring
Rheem Manufacturing has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Atlanta, GA, 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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