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About This Role
As a leader within the Analytics team, the Director, Analytics will play a pivotal role in shaping the future of the team, focusing on advanced analytics, statistical modeling, and data\-driven solutions that enhance performance media outcomes. This role will lead measurement strategy, experimentation frameworks, and analytics within clean room environments. This position offers a unique opportunity to help build and guide a team, contributing to the development of future brand strategies.
The Director will apply their years of experience in data analysis and strategic consulting as an analytic lead. Important to this success will be the ability to source and use the many data sources at the disposal of the analytics team to not only represent the business impact that our media/marketing campaigns have in the marketplace but also the ability to translate these insights into relevant recommendations to optimize media investment for our clients.
This role presents a unique opportunity to shape and build a high\-performing analytics team while contributing to the growth and transformation of the organization’s capabilities.
This role reports into the Vice President, Analytics.
Client Leadership \& Business Development
- Create professional relationships with the many members of your internal and client team(s) while being solely responsible for the level of quality in communications and results for the entire team that serves on your client's behalf.
- Work throughout the year with internal and external (client team, partner agencies/consulting groups) partners to create measurement plans/proposals that satisfy all client goals and address challenges/opportunities with clearly defined goals and definitions of program success.
- Coordinate the creation of the business rules and parameters of the engagement and participate together with internal Finance and Operations teams in the scoping process and any necessary requests for proposal from outside vendors/data companies.
- Manage the many internal data sources and (where applicable) third\-party vendors/data streams that comprise our custom measurement platform while keeping process on task within the established timelines and delivery dates.
- Synthesize the available data into insights about how successfully our programs transform target audience behavior. Find opportunities for program optimization and improvement.
- Be a strategic consultant using the aforementioned insights as the foundation for presentations, reports, assorted client deliverables in either a face\-to\-face or virtual setting.
- Identify areas of opportunity to expand our presence through consultative selling at current clients while periodically participating in the any new business development opportunities as they arise.
- Understand client needs and internal operations to achieve success for the client and team.
- Provide strategic review of your team's work to ensure deliverables are on time, data cleanliness \& clean presentation of story is presented, and overall, of high quality.
- Collaborate with people across the organization, affect positive change, understand competitive offerings, identify needs to get projects moving and keep them moving forward with our teams.
- Independently provide POV for clients or our teams, while sourcing or researching any needed information to inform decision\-making in consideration of business rules.
- Manage a team, teaching process and providing a growth path for direct reports while managing their workload for success.
Advanced Analytics \& Measurement* Lead development and application of statistical and predictive models to measure campaign performance
- Design and oversee A/B, incrementality, and experimental frameworks
- Apply and interpret Marketing Mix Modeling (MMM), including rapid MMM approaches
- Leverage clean room environments (e.g., Snowflake, Google ADH, Meta) for measurement, audience analysis, and data collaboration
- Translate complex modeling outputs into clear, actionable business insights for clients
People \& Team Development
- Manage and mentor Analytics Associate Directors, Managers and Senior Analysts, focusing on building the team’s capabilities, especially around large data analysis, measurement, and data visualization.
- Prepare the team for eventual direct reports and play a key role in the team’s growth, which will align with new brand initiatives and internal shifts.
- Participate in talent recruiting and retention efforts
- Foster a culture of continuous learning and collaboration within the team, with an emphasis on capability\-building and teaching.
- Collaborate across a broad range of agency collaborators, including business intelligence, media, content, engineering, and planning, in the development and refinement of capabilities.
- Lead capability\-building efforts within the team, sharing expertise in large datasets, measurement techniques, and technical skills like Python, SQL, and Excel.
- Drive forward\-thinking analytics across the organization, using insights to guide operational transformation.
- Manage prioritization and allocation of resources across projects and teams
- 8 to 10 years of experience in data science, advanced analytics, and statistical modeling
- 5\-6 years of client\-facing/consulting experience and 2\-4 years of management experience.
- Literacy of business intelligence software such as Datorama, Tableau, Power BI, Adobe, Google, etc.
- Advanced SQL proficiency, including complex joins, window functions, and pathing analysis across user journeys
- Programming experience in Python or R for modeling and advanced analysis
- Hands\-on experience with clean room environments (Snowflake, Google ADH, Meta, etc.)
- Experience designing and executing A/B and incrementality tests
- Exposure to or experience with Marketing Mix Modeling (MMM)
- Proven ability to lead and grow analytics teams, develop strategic client solutions, and influence senior decision\-makers.
- Degree in marketing, statistics, mathematics, or economics. An MBA or advanced degree is a plus.
Salary Context
This $167K-$185K range is below 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 Publicis Groupe, 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 ($176K) sits 20% below the category median. Disclosed range: $167K to $185K.
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.
Publicis Groupe AI Hiring
Publicis Groupe has 41 open AI roles right now. They're hiring across AI/ML Engineer, AI Engineering Manager, Data Scientist, AI Architect. Positions span Miami, FL, US, Boston, MA, US, New York, NY, US. Compensation range: $0K - $299K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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