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About This Role
### Job Title
Director, Data \& Analytics: AI Enablement \& Commercial Insights (Office: Nashville, TN)
### Job Description
Director, Data \& Analytics: AI Enablement \& Commercial Insights (Nashville, TN)
The Director, Data \& Analytics: AI Enablement \& Commercial Insights will lead the execution of scalable, governed, AI\-enabled data and analytics capabilities to accelerate commercial performance across Sales, Service, Marketing, and Commercial Operations, delivering governed data products, reporting solutions, and measurable business outcomes. Reporting to the Head of North America Data Strategy \& Analytics, this senior business\-facing partner will translate priorities into executable roadmaps and collaborate with IT, domain leaders, Commercial Operations, Finance and enterprise data teams to provide trusted data, modern platform capabilities, and AI\-ready infrastructure. The role requires an outcomes\-driven leader with deep implementation experience in cloud\-native data and AI platforms (e.g., Databricks, Azure, AWS Bedrock), governed LLM solutions, and familiarity with government/compliance requirements (FedRAMP, ATO, NIST, CMMC) to drive adoption and quantifiable commercial value.
Your role:
- Lead North America’s data, analytics, and AI execution agenda to drive speed, scalability, adoption, platform maturity, and measurable commercial impact across Sales, Service, Marketing, and Commercial Operations.
- Partner with IT to design, implement, govern, and scale secure, cloud\-native data and AI infrastructure (Databricks, Azure Data Lake, AWS Bedrock, enterprise AI/ML and BI tools) and integrate trusted data sources for generative and agentic AI workflows.
- Serve as a senior business partner to domain leaders, translating needs into prioritized analytics and AI roadmaps, owning the reporting/analytics backlog, and delivering governed data products and reporting solutions.
- Communicate insights through compelling storytelling to clarify performance drivers, identify opportunities, drive urgency and action, while leading data governance, quality, KPI standardization, stewardship, lineage, and compliance.
- Build and lead a high\-performing, hybrid centralized\-federated team focused on agile execution, responsible AI, technical curiosity, simplification, and outcomes\-based delivery, including readiness for U.S. Government/public\-sector requirements (FedRAMP, ATO, NIST, CMMC/DFARS, accessibility, procurement).
You're the right fit:
- 12\+ years in data strategy, analytics, AI enablement, digital/enterprise transformation or consulting, including 7\+ years of direct or matrixed leadership, with proven ability to move from strategy to implementation and measurable commercial results.
- Bachelor’s degree (advanced degree preferred) in Information Management, Data Management, Data Analytics, Computer Science, Information Technology or equivalent; healthcare, medical device, or regulated\-industry experience strongly preferred.
- Deep domain expertise in commercial, sales, service, marketing, and healthcare analytics, connecting data and AI to revenue growth, margin expansion, productivity, customer experience, and operational performance.
- Strong technical and governance experience implementing and scaling modern data/AI platforms (Databricks, Azure Data Lake, AWS Bedrock, cloud\-native engineering, AI/ML, BI, governed LLMs), plus working knowledge of U.S. Government/public\-sector requirements (FedRAMP, ATO, NIST, CMMC/DFARS, Section 508, federal procurement).
- Outcomes\-driven leader with agile delivery discipline, exceptional executive storytelling, focus on quality/governance/privacy/security.
- You must be able to successfully perform the following minimum Physical, Cognitive and Environmental job requirements with or without accommodation for this position .
How we work together
We believe that we are better together than apart. For our office\-based teams, this means working in\-person at least 3 days per week. Onsite roles require full\-time presence in the company’s facilities. Field roles are most effectively done outside of the company’s main facilities, generally at the customers’ or suppliers’ locations.
This is an office based role.
About Philips
We are a health technology company. We built our entire company around the belief that every human matters, and we won't stop until everybody everywhere has access to the quality healthcare that we all deserve. Do the work of your life to help the lives of others.
- Learn more about our business .
- Discover our rich and exciting history .
- Learn more about our purpose .
- Learn more about our culture.
Philips Transparency Details
The pay range for this position in Nashville, TN is $159,000 to $254,400\.
The actual base pay offered may vary within the posted ranges depending on multiple factors including job\-related knowledge/skills, experience, business needs, geographical location, and internal equity.
In addition, other compensation, such as an annual incentive bonus, sales commission or long\-term incentives may be offered. Employees are eligible to participate in our comprehensive Philips Total Rewards benefits program, which includes a generous PTO, 401k (up to 7% match), HSA (with company contribution), stock purchase plan, education reimbursement and much more. Details about our benefits can be found here .
At Philips, it is not typical for an individual to be hired at or near the top end of the range for their role and compensation decisions are dependent upon the facts and circumstances of each case.
Additional Information
US work authorization is a precondition of employment. The company *will not* consider candidates who require sponsorship for a work\-authorized visa, now or in the future.
Company relocation benefits will not be provided for this position. For this position, you must reside in or within commuting distance to Nashville, TN.
This requisition is expected to stay active for 45 days but may close earlier if a successful candidate is selected or business necessity dictates. Interested candidates are encouraged to apply as soon as possible to ensure consideration.
*Philips is an Equal Employment and Opportunity Employer including Disability/Vets and maintains a drug\-free workplace.*
Salary Context
This $159K-$254K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Philips, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($206K) sits 6% below the category median. Disclosed range: $159K to $254K.
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.
Philips AI Hiring
Philips has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Nashville, TN, US. Compensation range: $170K - $254K.
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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