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About This Role
AI for Science, Research Intelligence \& Knowledge Discovery
Lead the Teams Building AI That Advances Science
What if the teams you lead could help accelerate scientific breakthroughs, improve healthcare outcomes, and expand human knowledge?
At Elsevier, data science leadership is about far more than managing projects, models, or roadmaps. It is about leading teams that build intelligent systems enabling researchers, clinicians, educators, and institutions to discover evidence, connect ideas, uncover insights, and solve some of the world's most important challenges.
Every day, millions of researchers depend on our products to navigate an ever\-growing universe of scientific knowledge. As a Data Science Leader, your work will directly influence how knowledge is discovered, understood, trusted, and applied across the global research ecosystem.
This is leadership with purpose. This is AI in service of science.
About the team
As part of a growing team of Data Scientists, you will take on some of the hardest problems in science. This team is building intelligent systems that can reason across scientific publications, research data, knowledge graphs, ontologies, metadata, taxonomies, citations, and content spanning every scientific discipline.
About the Role
As a Data Science Leader, you will build, develop, and inspire high\-performing teams responsible for delivering advanced AI, machine learning, search, retrieval, NLP, and generative AI solutions that power scientific discovery and research intelligence.
You will provide strategic direction, elevate technical excellence, and help shape the future of AI\-enabled products used by researchers and healthcare professionals worldwide. Working at the intersection of cutting\-edge technology and meaningful impact, you will guide teams solving some of the most complex and intellectually challenging problems in science.
Success in this role requires a balance of technical depth, people leadership, strategic thinking, and a passion for helping others do their best work while advancing a mission that matters.
What You'll Do
- Lead and develop high\-performing teams of data scientists, machine learning engineers, researchers, and technical contributors.
- Define and execute data science strategies that advance scientific discovery, research intelligence, and knowledge\-access products.
- Drive the development of AI\-powered capabilities across search, retrieval, recommendation, NLP, knowledge systems, and generative AI.
- Translate complex customer, scientific, and business challenges into scalable data science solutions and measurable outcomes.
- Establish high standards for experimentation, evaluation, model quality, reliability, and responsible AI practices.
- Partner closely with Product, Engineering, Research, UX, Analytics, and domain experts to shape product strategy and delivery.
- Mentor and coach team members while fostering a culture of scientific rigor, collaboration, innovation, and continuous learning.
- Guide the adoption of emerging AI technologies, including LLMs, retrieval\-augmented generation, semantic search, and knowledge\-based systems.
- Influence senior stakeholders and contribute to long\-term AI, technology, and product strategy across the organization.
- Ensure that AI systems are trustworthy, scalable, explainable, measurable, and aligned with meaningful customer and societal outcomes.
What We're Looking For
- Significant experience leading data science, machine learning, artificial intelligence, NLP, information retrieval, or related technical teams.
- Proven success building, coaching, and developing high\-performing teams in complex technology or product environments.
- Technical expertise across machine learning, generative AI, large language models, retrieval systems, experimentation, and model evaluation.
- Experience delivering AI\-powered products or platforms from concept through production deployment and measurable impact.
- Deep understanding of modern AI approaches, including LLMs, RAG architectures, semantic search, embeddings, and knowledge systems.
- Experience establishing evaluation frameworks, experimentation practices, and performance metrics for AI solutions.
- Ability to translate ambiguous challenges into clear strategy, execution plans, and business outcomes.
- Exceptional communication and stakeholder\-management skills with the ability to influence technical, product, and executive audiences.
- Experience working with large\-scale structured, semi\-structured, and unstructured data in production environments.
- A passion for advancing science, expanding access to knowledge, developing people, and applying AI to create meaningful real\-world impact.
Why Join Elsevier
Because your leadership will matter.
You will lead teams building AI systems that help researchers discover knowledge faster, assess evidence more effectively, generate new insights, and accelerate scientific progress.
\&\#xa;\&\#xa;U.S. National Base Pay Range: $115,400 \- $192,300\. Geographic differentials may apply in some locations to better reflect local market rates.\&\#xa;\&\#xa;If performed in Maryland, the base pay range is $121,200 \- $201,900\.If performed in New York, the base pay range is $126,900 \- $211,500\.If performed in New York City, the base pay range is $138,400 \- $230,700\.If performed in Rochester, NY, the base pay range is $115,400 \- $192,300\.If performed in New Jersey, the base pay range is $136,213 \- $217,587\.\&\#xa;\&\#xa;This job is eligible for an annual incentive bonus.\&\#xa;\&\#xa;\&\#xa;\&\#xa;\&\#xa;\&\#xa;
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Salary Context
This $115K-$217K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At RELX Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($166K) sits 8% below the category median. Disclosed range: $115K to $217K.
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.
RELX Group AI Hiring
RELX Group has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span CA, US, Philadelphia, PA, US, Raleigh, NC, US. Compensation range: $192K - $281K.
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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