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About This Role
Company Description
The Director, Product, AI Commerce Analytics \& Data Strategy owns the AI and data strategy and the supporting architecture for NIQ's AI Commerce partnerships and AI\-driven product operations. The role initiates the approaches, methodologies, and standards that other teams then execute and operate, and is regarded as NIQ's functional authority on how its data assets translate into the agentic\-commerce environment. Within NIQ's Artificial Intelligence (AI) Commerce function, this role works alongside the Director, Product, AI Commerce and the Director, Product, AI Commerce Product Partnerships; the SVP, AI Commerce leads the overall AI Commerce strategy, including how the product, partnerships, and Commerce Lab pillars connect. It exists to define what NIQ's data products can credibly promise as commerce moves from shelf and search to agent\-mediated discovery, to set the technical and strategic standards that keep those promises defensible, and to carry that strategic data positioning directly into active, pre\-contract partner negotiations. It operates as an individual contributor, driving outcomes through expertise, originated standards, stakeholder persuasion, and an analytical, data\-driven approach rather than through direct reports.
Job Description Strategy \& Vision
- Set NIQ's AI and data strategy for AI Commerce partnerships and products that establish NIQ's role in the AI Commerce Platform ecosystem.
- Anticipate where NIQ's data and AI architecture needs to evolve as commerce moves toward agent\-mediated discovery, and originate the framing the function executes against, including concepts such as share of agent recommendation as the successor to share of shelf and an Agent Readiness methodology.
- Originate net\-new methodologies where no established approach exists, prove them through bounded pilots, and transfer them to a standing team.
- Provide functional thought leadership on agentic commerce to the SVP, AI Commerce and other executive stakeholders.
AI \& Data Architecture Ownership
- Develop the architecture for AI\-driven systems across the partnership and product\-operations portfolio, including multi\-agent system design, knowledge\-base structure, and the instruction and governance standards that keep those systems consistent and domain\-accurate.
- Establish AI\-tooling governance as a discipline distinct from process governance, with its own owner, cadence, and standards, and maintain that boundary as the organization adopts it.
- Research, organize, document, and communicate the critical data and information inputs required for AI Commerce partnerships, product, and Commerce Lab to succeed, with the goal of getting dependent teams’ commitment to resource AI Commerce needs
Stakeholder and Executive Leadership
- Translate deep technical AI and data mechanics into terms executive and partner stakeholders can act on
- Using a variety of tactics, communicate complex architecture and governance decisions to senior stakeholders, anticipate objections, and persuade others, often at senior levels, to adopt a different point of view where warranted.
- Represent the AI and data strategy in AI Commerce leadership discussions, bringing recommendations to the SVP, AI Commerce and other executive stakeholders
- Partner with the Director, Product, AI Commerce and the Director, Product, AI Commerce Product Partnerships to align architecture and governance standards with product and partnership priorities.
Qualifications* At least 15 years of relevant digital, technology, or AI experience, spanning e\-commerce, demand generation, CRM, and applied AI architecture, with a track record of being regarded as a functional expert in this discipline
- Working practitioner\-level command of current AI platforms and techniques, including multi\-agent architecture, knowledge\-base design, and prompt engineering.
- A track record of originating methodologies and standards where none exist, proving them through bounded pilots, and transferring them to a standing team.
- A track record of anticipating where a technical discipline needs to move next and recommending new concepts, techniques, or standards ahead of the organization's current state.
- Comfort operating as an individual contributor, driving outcomes through expertise and stakeholder persuasion rather than formal authority over a team.
- Demonstrated ability to translate technical and strategic positioning directly into partnership and product decisions, and to persuade senior stakeholders to adopt recommendations.
Additional Information *This role has a market\-competitive salary with an anticipated base compensation of the following range: $110,000 \- $180,000\. Actual salaries will vary depending on a candidate’s experience, qualifications, skills, and location. This role might also be eligible for a sales\-based incentive or performance\-based bonus. Other benefits include flexible working environment, comprehensive health insurance, industry\-leading parental leave, life insurance, education support and more*
\#LI\-Remote
Our Benefits
- Flexible working environment
- Volunteer time off
- LinkedIn Learning
- Employee\-Assistance\-Program (EAP)
NIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job\-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles: https://nielseniq.com/global/en/info/niqs\-ai\-safety\-policies/
About NIQ
NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights—delivered with advanced analytics through state\-of\-the\-art platforms—NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100\+ markets, covering more than 90% of the world’s population.
For more information, visit NIQ.com
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Our commitment to Diversity, Equity, and Inclusion
At NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence. All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the https://nielseniq.com/global/en/news\-center/diversity\-inclusion
Salary Context
This $110K-$180K 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 NielsenIQ, 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 ($145K) sits 34% below the category median. Disclosed range: $110K to $180K.
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.
NielsenIQ AI Hiring
NielsenIQ has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in CT, US. Compensation range: $180K - $180K.
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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