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We are seeking a Sr. Director, Data \& AI Advisory to lead Acxiom’s Data \& AI Advisory team within the broader Data \& AI Services practice. This role is for a senior consulting leader who can advise clients on how to turn customer data, identity, platforms, analytics, and AI into measurable business value while helping scale a growing advisory business.
You will serve as a trusted advisor to senior client stakeholders, lead complex consulting engagements, and own day\-to\-day leadership of a portfolio of advisory and delivery\-oriented offerings. Success requires the ability to shape the market story, sell the work, guide multidisciplinary teams, and translate strategic ambition into practical roadmaps, operating models, implementation plans, and value cases.
This is not a purely conceptual strategy role. The right candidate will understand how data strategy, platform modernization, identity, governance, activation, measurement, and AI enablement come together in real marketing and customer engagement environments. They will be expected to structure and sell offerings, lead delivery, mentor team members, develop reusable methods, and ensure our recommendations are commercially relevant, technically grounded, and executable.*SPONSORSHIP NOT AVAILABLE FOR THIS POSITION*
What You Will Do
Advisory \& Engagement Leadership
- Lead Data \& AI Advisory engagements from opportunity shaping through discovery, current\-state assessment, future\-state design, roadmap development, and executive readout.
- Serve as the senior day\-to\-day advisory lead for clients, building trusted relationships with marketing, data, technology, analytics, operations, privacy, and executive stakeholders.
- Translate complex business challenges into clear data, AI, analytics, governance, identity, and technology strategies tied to measurable outcomes.
- Structure ambiguous problems into workplans, hypotheses, stakeholder sessions, deliverables, decision points, and actionable recommendations.
Offering Leadership \& Commercial Growth
- Lead and evolve a portfolio of Data \& AI Advisory offerings across strategy, readiness, platform improvement, identity enablement, activation, measurement, and value realization.
- Support business development through opportunity qualification, solution shaping, proposal writing, pitch development, scope definition, estimation, and delivery model design.
- Develop repeatable playbooks, sales enablement materials, assessment frameworks, workshop formats, delivery standards, and accelerators that improve quality and scalability.
Data, AI \& Marketing Transformation Strategy
- Advise clients on how to use first\-, second\-, and third\-party data as strategic assets for customer understanding, marketing effectiveness, personalization, measurement, and growth.
- Assess client maturity across data strategy, platform utilization, identity resolution, consent management, governance, data quality, analytics, activation, measurement, and AI readiness.
- Develop future\-state recommendations across operating model, capability gaps, data flows, functional architecture, governance, measurement, business case, and implementation sequencing.
- Guide clients on responsible and effective AI adoption, including use case prioritization, data readiness, workflow integration, governance, change management, and value realization.
Solutioning, Delivery \& Cross\-Functional Collaboration
- Partner with solution architects, data engineers, data scientists, analysts, product owners, experience strategists, and technology partners to ensure strategy is grounded in delivery reality.
- Deliver executive\-ready artifacts such as maturity assessments, use case portfolios, gap analyses, future\-state designs, data flows, solution concepts, RACIs, business cases, and phased roadmaps.
- Bridge strategy and implementation by clarifying scope, sequencing, dependencies, success metrics, delivery roles, risks, and decision rights.
Team Leadership \& Practice Development
- Provide day\-to\-day leadership, mentorship, and coaching to Data \& AI Advisory consultants, strategists, and analysts.
- Shape staffing plans, delivery roles, quality standards, reusable methods, and development paths for the advisory team.
- Translate practice goals into tactical actions and ensure progress against priorities.
- Support the practice leader in scaling the broader practice by identifying talent needs, capability gaps, offering opportunities, and operational improvements.
What You Will Have
- 8\+ years of professional experience in consulting or professional services, with a focus on data strategy, marketing transformation, analytics, AI, data management, technology advisory, or related fields.
- Bachelors Degree Required \- Masters Preferred
- Proven experience leading client\-facing consulting engagements end to end, including discovery, assessment, strategy, roadmap, executive presentation, and implementation planning.
- Experience developing, packaging, selling, and leading consulting offerings or repeatable service propositions, including sales enablement, scoping, proposal development, and value articulation.
- Strong understanding of customer data as a strategic asset in marketing and customer engagement, including first\-party data, identity, segmentation, activation, measurement, and personalization.
- Working knowledge of modern data and marketing ecosystems, including cloud data platforms, CDPs, CRM, activation platforms, journey orchestration, clean rooms, and measurement tools.
- Practical understanding of AI and analytics use cases, including how data readiness, governance, workflow integration, operating model design, and adoption affect value realization.
- Working awareness of data governance, data quality, data cataloging, privacy\-by\-design, consent management, and responsible AI considerations, with the ability to involve deeper SMEs when needed.
- Experience leading cross\-functional teams that may include strategists, analysts, solution architects, data engineers, data scientists, product owners, privacy stakeholders, and client SMEs.
- Ability to translate ambiguous executive priorities and non\-technical stakeholder needs into clear requirements, strategic recommendations, workplans, roadmaps, and business cases.
- Strong executive communication, facilitation, presentation, and storytelling skills, with the ability to influence both technical and non\-technical audiences.
- Experience applying consulting frameworks such as maturity assessments, capability models, use case prioritization, value vs. effort analysis, operating model design, governance models, and roadmaps.
- Experience working across onshore, nearshore, offshore, intercultural, and interdisciplinary teams.
Nice to Have
- Experience in marketing, media, financial services, healthcare, automotive, retail, or other data\-rich industries.
- Experience with AdTech, experimentation, audience strategy, data collaboration, conversion APIs, or advanced measurement approaches such as incrementality, attribution, MMM inputs, or lift analysis.
- Familiarity with web and app analytics platforms, data visualization tools, customer intelligence platforms, or marketing measurement platforms.
- Familiarity with machine learning concepts, statistics, empirical economics, optimization, scenario planning, or AI\-enabled decisioning in a marketing or customer context.
- A quantitative, technical, or business background in business, economics, computer science, business informatics, data science, analytics, or a related discipline.
Why Join Us
You will help lead a team operating at the forefront of data, AI, identity, and marketing transformation. Our work spans early\-stage strategy through enterprise\-scale change, helping clients build modern, insight\-driven customer data ecosystems that are powerful, practical, and measurable.
In this role, you will shape how leading organizations define customer data strategy, modernize marketing and analytics capabilities, operationalize AI\-enabled decisioning, improve platform performance, create practical governance models, and turn strategic ambition into executable roadmaps. You will also help scale the Data \& AI Advisory team by developing people, improving delivery standards, shaping offerings, and partnering with the broader practice to drive growth.
Primary Location City/State:
Homebased \- Conway, ArkansasAdditional Locations (if applicable):
Acxiom is an equal opportunity employer, including disability and protected veteran status (EOE/Vet/Disabled) and does not discriminate in recruiting, hiring, training, promotion or other employment of associates or the awarding of subcontracts because of a person's race, color, sex, age, religion, national origin, protected veteran, military status, physical or mental disability, sexual orientation, gender identity or expression, genetics or other protected status.
Attention California Applicants: Please see our CCPA/CPRA Privacy Act notice here.
Attention Colorado, California, Connecticut, Maryland, Nevada, New Jersey, New York City, Ohio, Rhode Island, and Washington Applicants: This position is not located in the aforementioned locations but applications for remote work may be considered. For information about this role under state or local equal pay or pay transparency laws, please contact [email protected].
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Acxiom, 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 in Demand for This Role
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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Acxiom AI Hiring
Acxiom has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Conway, AR, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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