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About This Role
Job Description:
Sr Manager Growth AI Transformation
Function: Global Growth — AI Transformation
Reports to: Global Head AI Transformation, Global Growth
Level: Senior Individual Contributor – T3
Location: Chicago, IL (must be based in, or relocate to, Chicago)
About the Role
This is a newly created senior role at the heart of a large-scale AI transformation agenda within Mars Snacking. The Sr Manager, Growth AI Transformation will work directly with the Global Head AI Transformation to identify, develop, and scale AI-powered initiatives across the Global Growth function and across regional and local markets worldwide.
This is a business role, not a technology role. The ideal candidate is a commercially grounded CPG professional who understands how brands grow through marketing, sales, and consumer insights, and who has used data and AI to accelerate that growth. You will bring enough technical fluency to work credibly alongside engineers, data architects, and platform partners, but you will not build models, write code, or design technical architecture; our technical teams and partners do that. You will be the connective tissue between business ambition and technical execution, orchestrating a broad portfolio of initiatives, managing external partners, and ensuring that what gets built actually lands and creates impact.
Key Responsibilities
- AI Opportunity Scanning Industry Intelligence
- Continuously monitor the external AI landscape: tools, platforms, competitor moves, and partner capabilities, to identify where AI can create the most value for our brands and for our Marketing, Sales, and Human Insights teams.
- Translate external signals into opportunity briefs that shape the portfolio roadmap and inform leadership decisions.
- Maintain a point of view on where the industry is heading, ensuring the teams pipeline stays ahead of the curve rather than catching up to it.
- Portfolio Orchestration Prioritisation
- Grow and manage a mixed portfolio of AI initiatives, including flagship engines and smaller rapid experiments, ensuring appropriate rigour at each stage of development.
- Inform strategic direction of initiatives to support AI transformation and building of new capabilities; identify and manage interdependencies with enterprise-wide AI plays (e.g. Gemini Enterprise, ODAI, Tech-fuelled sales); identify AI risks and implement guardrails; orchestrate across workstreams to drive efficiencies; and ensure a seamless end-to-end user experience across initiatives.
- Identify and facilitate escalations and decision-points to and from cross-functional implementation teams and leadership to ensure that timely, informed, evidence-based prioritisation decisions are made across competing demands and transformed into tangible next steps.
- Business Case Development Stakeholder Alignment
- Lead the structuring and articulation of business cases for new AI initiatives, working in close partnership with Digital, ODDA, Finance Analytics, and other functions to source funding, to model and measure ROI.
- Own the narrative, translating technical possibilities and data inputs into compelling proposals that secure senior approval and budget.
- Build and maintain strong relationships with stakeholders across the Global Growth function and in regional markets, and lead discussions in key leadership forums.
- Agile Delivery Oversight External Partner Management
- Act as the strategic manager across rapid/agile delivery teams and external partners (e.g. Writer, Google, Fractal, Publicis).
- Define ways of working, set clear deliverables and accountability structures, and ensure development stays aligned to intended business outcomes.
- Apply sufficient technical fluency to challenge, unblock, and redirect technical and partner teams where needed, without replacing the engineers and data scientists.
- Scaling, Governance Change Enablement
- Own the path from pilot to scale for validated initiatives, navigating the varying governance and compliance processes across regions.
- Coordinate deployment prioritisation across initiatives and inform the development of training and communication materials in partnership with Capability Building teams.
- Drive adoption by ensuring that what gets built is understood, embraced, and actually used by the teams its designed for by aligning to Mars’s growth objectives and through continuous user feedback loops.
What Were Looking For:
- CPG / FMCG background required, ideally in snacking, confectionery, or food beverage, with hands-on experience in a commercial function (Marketing, Brand, Sales, Revenue Growth Management, or Consumer Insights) or in strategy consulting to those functions.
- 8 years of experience in commercial strategy, product strategy, or digital/AI transformation roles within complex organisations, including leading data or AI-enabled products that serve commercial teams.
- Demonstrated experience growing a portfolio of advanced analytical products from ideation through to scaling, demonstrating strong ROI delivery.
- Strong business case authorship, the ability to build thewhy this, why now, why us narrative for senior audiences.
- Demonstrated experience writing contracts, managing and holding accountable external agencies or technology partners.
- Demonstrated experience building cutting-edge advanced analytical capabilities and managing cross-functional teams through a complex organization.
- Technical fluency sufficient to engage credibly with data scientists, architects, engineers, responsible AI board and AI platform teams. This is not a hands-on engineering, AI architecture, or software development role.
- Comfort operating in a broad, fast-moving portfolio, able to switch between strategic thinking and hands-on delivery.
- Outstanding communication and stakeholder management skills across cultures and seniority levels.
Key Assumptions:
- Assumes dedicated support from Finance and Data Analytics for quantitative modelling within business cases.
- Assumes dedicated support from the Data Office for data governance (this role bridges data stewardship within the business; it does not lead).
- Assumes access to project management resource (internal or partner) for delivery coordination on larger initiatives.
- Assumes Digital Technologies and Data Architecture own technical design and infrastructure decisions; this role bridges, not replaces, that expertise.
- Assumes Brand, Human Insights, and Growth Capabilities associates are embedded into initiative teams as subject-matter contributors.
- Assumes external partners are already contracted or in a procurement pipeline; this role manages the work, not the commercial relationship.
The base pay range for this position at commencement of employment is between the range listed below, however, base pay offered may vary depending on multiple individualized factors, including but not limited to job-related knowledge, skills, and experience. The total compensation package for this position will include variable pay, medical and dental benefits, participation in 401k plan, and paid time off benefits. Details of participation in these benefit plans will be provided if an applicant receives an offer of employment.
The pay range shown is applicable to all US locations listed within the job posting: USD 169,840.00 - USD 233,530.00
Salary Context
This $169K-$233K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Mars, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($201K) sits 5% below the category median. Disclosed range: $169K to $233K.
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.
Mars AI Hiring
Mars has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $233K - $233K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% below the national 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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