Interested in this AI/ML Engineer role at ZS Associates?
Apply Now →About This Role
: ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side-by-side with a powerful collective of thinkers and experts shaping life-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life-changing impact to ZS.
: Growth Marketing Lead - Technology, Insights, Analytics, AI
ZS is a place where passion changes lives. As a management consulting and technology firm focused on improving life and how we live it, we transform ideas into impact by bringing together data, science, technology and human ingenuity to deliver better outcomes for all. Here you’ll work side-by-side with a powerful collective of thinkers and experts shaping life-changing solutions for patients, caregivers and consumers, worldwide. ZSers drive impact by bringing a client-first mentality to each and every engagement. We partner collaboratively with our clients to develop custom solutions and technology products that create value and deliver company results across critical areas of their business. Bring your curiosity for learning, bold ideas, courage and passion to drive life-changing impact to ZS.
ZS is seeking a Consulting Marketing Lead - Technology, Insights, Analytics, AI to lead significant components of consulting marketing and portfolio strategy work across ZS’s technology, insights, analytics and AI capabilities. This role will partner closely with business leaders, consulting teams, Demand Generation and other marketing functions to translate business priorities, market insights, competitive intelligence, client needs and ZS expertise into clear positioning, messaging, portfolio direction, strategic recommendations and sales enablement materials.
You will develop the strategic foundations that guide marketing investments and go-to-market plans, ensuring that audience engagement, content, thought leadership and sales enablement are aligned to business objectives and delivered with impact across the marketing ecosystem. This role does not directly own campaign activation or execution; instead, it partners with Demand Generation and other marketing functions to ensure activation is grounded in clear strategy, positioning and audience insight.
This role requires independent thinking, stakeholder influence and the ability to connect customer insight, competitive intelligence, positioning, messaging and sales enablement needs to business impact. Success requires strong analytical judgment, strategic thinking, executive communication and the ability to translate complex market and business information into clear direction and action.
What You’ll Do:
Market strategy, portfolio direction and growth planning
- Develop a deep understanding of ZS’s technology, insights, analytics and AI capabilities, including market opportunities, priority offerings, competitive dynamics, customer needs and stakeholder priorities.
- Lead market opportunity assessments and growth analyses to identify new business opportunities.
- Translate business priorities, market insights and client needs into portfolio strategy recommendations, growth strategies, positioning and market narratives.
- Contribute to annual marketing plans, quarterly business reviews, launch planning and portfolio evolution for supported solution areas, practices and ventures.
- Identify new opportunity areas or marketing claims that deliver competitive advantage.
Positioning, messaging and market-backed narratives
- Develop business and stakeholder strategies and messaging that are well-aligned to company brand, priorities and key messages.
- Develop positioning, messaging frameworks, value propositions and executive-ready narratives that support clear market differentiation.
- Partner with business leaders and SMEs to translate market trends, competitive intelligence, client needs, ZS expertise and business priorities into market-backed points of view and strategic artifacts.
- Collaborate on strategic narrative and thought leadership planning to identify differentiated points of view.
- Lead creative processes and brainstorming to identify novel solutions.
Thought leadership, strategic artifacts and sales enablement
- Partner with business leaders, consulting teams and Marcomms colleagues to develop market-backed strategic artifacts, thought leadership inputs and intellectual capital.
- Translate strategic narratives, market insights and positioning into sales enablement materials, proof-point packages and outreach tools that client-facing teams can use in business development conversations.
- Support development of frameworks, trend summaries, practice approach collateral, executive-ready materials and other strategic artifacts that can be amplified through campaigns and client engagement.
- Help business leaders connect portfolio decisions, market priorities and messaging to enterprise objectives and client needs.
- Partner with Demand Generation and other marketing functions to ensure go-to-market plans are grounded in clear strategy, positioning and audience insight.
Insights, analytics and performance learning
- Integrate multiple data sources, including customer insight, competitive intelligence, portfolio analysis and marketing performance data, to solve ambiguous strategic problems.
- Analyze marketing campaign data to deliver insights and recommendations.
- Use insights to inform messaging, portfolio direction, strategic recommendations and future planning.
- Support structured documentation of strategies, strategic artifacts, assets and outcomes so learnings can inform future planning and decision making.
Stakeholder leadership and cross-functional alignment
- Independently drive stakeholder discussions and alignment with business leaders, consulting teams and cross-functional marketing partners.
- Serve as a strategic thought partner to supported business areas, helping leaders assess where to play, how to win and how to align marketing and portfolio decisions to enterprise priorities.
- Own complex, multi-phase projects, managing deliverables to timelines and budgets.
- Effectively prioritize and actively manage cross-functional team delivery and alignment.
- Develop clear, executive-ready recommendations, presentations and decision materials.
- Mentor and invest in others for team success.
What You’ll Bring:
- 5–8 years of experience in marketing strategy, portfolio strategy, commercial strategy, consulting or related advisory roles.
- Strong experience developing recommendations, business cases, value propositions, positioning, messaging or executive-ready materials.
- Strong analytical skills and comfort integrating multiple data sources into a cohesive story.
- Ability to translate complex technology, insights, analytics and AI topics into clear decisions, actions and strategic narratives.
- Effective stakeholder management and ability to influence cross-functional partners.
- Ability to lead moderately complex workstreams independently and manage ambiguity.
- Strong communication skills with the ability to translate complex insights into clear, persuasive recommendations.
- Experience connecting customer insight, competitive intelligence, market analysis, portfolio priorities and business objectives to strategic direction.
- Ability to develop market-backed narratives, value propositions, sales enablement materials and thought leadership inputs.
- Fluency in English.
- Client-first mentality, collaborative spirit and problem-solving approach.
: How you’ll grow:
- Cross-functional skills development & custom learning pathways
- Milestone training programs aligned to career progression opportunities
- Internal mobility paths that empower growth via s-curves, individual contribution and role expansions
Perks & Benefits:
At ZS, your growth matters. We offer a comprehensive total rewards package that supports your health and well‑being, financial future, time away, and professional development. With robust skills‑building programs, multiple career progression paths, internal mobility, and a deeply collaborative culture, you’ll have the opportunity to do meaningful work, expand your capabilities, and thrive as part of a global community. For details on total rewards in United States, visit ZS US office locations | Where we work | ZS.
Hybrid working model:
We are committed to giving our employees a flexible and connected way of working. A flexible and connected ZS allows us to combine work from home and on-site presence at clients/ZS offices for the majority of our week. The magic of ZS culture and innovation thrives in both planned and spontaneous face-to-face connections.
Travel:
Travel is a requirement at ZS for client facing ZSers; business needs of your project and client are the priority. While some projects may be local, all client-facing ZSers should be prepared to travel as needed. Travel provides opportunities to strengthen client relationships, gain diverse experiences, and enhance professional growth by working in different environments and cultures.
Considering applying?
At ZS, we honor the visible and invisible elements of our identities, personal experiences, and belief systems—the ones that comprise us as individuals, shape who we are, and make us unique. We believe your personal interests, identities, and desire to learn are integral to your success here. We are committed to building a team that reflects a broad variety of backgrounds, perspectives, and experiences. Learn more about our inclusion and belonging efforts and the networks ZS supports to assist our ZSers in cultivating community spaces and obtaining the resources they need to thrive.
If you’re eager to grow, contribute, and bring your unique self to our work, we encourage you to apply.
ZS is an equal opportunity employer and is committed to providing equal employment and advancement opportunities without regard to any class protected by applicable law.
Work Authorization:
This position is not eligible for visa sponsorship. Candidates must have authorization to work in the United States that does not now or in the future require employer sponsorship.
To complete your application:
An on-line application, including a full set of transcripts (official or unofficial), is required to be considered.
NO AGENCY CALLS, PLEASE.
Find Out More At:
www.zs.com
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At ZS Associates, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
ZS Associates AI Hiring
ZS Associates has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Princeton, NJ, US, Chicago, IL, US.
Location Context
AI roles in Chicago pay a median of $194,700 across 390 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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