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About This Role
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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.
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Insights \& Analytics ZS's Insights \& Analytics group partners with clients to design and deliver solutions to help them tackle a broad range of business challenges. Our teams work on multiple projects simultaneously, leveraging advanced data analytics and problem\-solving techniques. Our recommendations and solutions are based on rigorous research and analysis underpinned by deep expertise and thought leadership.
We are seeking a Data Scientist (Associate Consultant) with deep expertise in both Generative AI and traditional machine learning to join our dynamic team. In this role, you will be responsible for designing, developing, and deploying high\-impact AI solutions.
You will be a key player in architecting our next generation of AI agents and workflows that tackle complex problems, turning ambiguous business needs into concrete technical solutions that redefine what's possible.
This is a hands\-on role that requires a strong foundation in ML theory, advanced skills in GenAI application development, and a practical understanding of deploying models to ensure your creations are robust, scalable, and impactful.
What You’ll Do* Design, build, and deploy advanced machine learning and GenAI models to solve key business problems.
- Architect and develop complex, stateful multi\-agent systems and workflows using modern agentic frameworks like LangGraph
- Develop and productionize sophisticated AI systems, including fine\-tuning open\-source LLMs, building advanced Retrieval\-Augmented Generation (RAG) pipelines, experience in advanced prompt engineering development and designing multi\-agent systems.
- Lead the research, experimentation, and implementation of novel GenAI techniques to solve complex business problems, pushing the boundaries of what is possible.
- Lead the end\-to\-end lifecycle of data science projects, from initial conception and data analysis to model deployment and in\-production monitoring.
- Apply classical machine learning techniques (e.g., predictive modeling, time\-series analysis, classification) to analyze large\-scale, complex datasets.
- Collaborate closely with cross\-functional teams, including engineers, product managers, and domain experts, to deliver integrated AI solutions.
- Ensure the operational viability of deployed models by implementing robust monitoring for performance and data drift.
- Provide guidance and mentorship to Associate team members.
What You’ll Bring* A PhD Degree in Computer Science, Statistics, or a relevant field or a master’s degree with 3\-5 years of relevant post\-collegiate work experience;
- Demonstrated experience in designing, building, and deploying Generative AI applications, with hands\-on expertise in techniques such as fine\-tuning, prompt engineering, Agentic systems, Retrieval\-Augmented Generation (RAG), and working with vector databases.
- Strong proficiency in Python and core data science libraries (e.g., pandas, scikit\-learn).
- Experience with deep learning frameworks such as PyTorch or TensorFlow.
- Prior leadership experience from a relevant position.
- Fluency in English.
Additional Skills:* Deep, hands\-on experience building multi\-agent systems with frameworks like LangChain, LangGraph, or similar Agent Development Kits (ADKs).
- Deep experience with GenAI frameworks like LangChain, LlamaIndex, Hugging Face ecosystem \& familiarity to MCP tools.
- Experience with model quantization, inference optimization, and other techniques for the efficient deployment of large models.
- Experience with cloud platforms (AWS, GCP, or Azure) and their AI/ML services.
- A strong track record of applying data science to solve complex, real\-world problems.
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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.
To complete your application:
Candidates must possess or be able to obtain work authorization for their intended country of employment. 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 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 $178,940 based on 11,900 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $97,380.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
ZS Associates AI Hiring
ZS Associates has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Princeton, NJ, US, Bellevue, WA, US, South San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% above 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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