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About This Role
LexisNexis Risk Solutions is seeking a Sr Director, Business Enablement and Strategic Insights to drive operational excellence within our internal operations. This role will focus on leveraging AI applications to enhance business productivity and agility, streamline workflows, and deliver measurable efficiency gains across the organization.
This leader will work closely with the Enterprise AI Guild to champion and accelerate adoption of AI tools to drive better, faster, data\-informed decisions across the organization, while ensuring that teams balance (a) speed of AI adoption, (b) a philosophy of “extreme reuse” to promote tool/knowledge sharing, and (c) the implementation of safe and responsible AI practices. Key focus will be on partnering with business and technology leaders to create a baseline of current AI usage, create and share use cases across the organization, identify and assess needed tooling, minimize redundant AI tools, and enable systematic tracking and senior\-level reporting of impact.
This leader will also lead competitive intelligence and customer insight programs — ensuring that our businesses are both operationally smarter and strategically sharper.
This role requires exceptional analytical, business, and leadership capabilities, with demonstrated ability to work cross\-functionally with executive management, functional teams, and key stakeholders. The ideal candidate thrives in a fast\-paced environment, excels at optimizing complex processes, has a strong knowledge of emerging AI technologies (such as Generative AI and Agentic AI) and their applications into workflows, and has a proven track record of enhancing operational efficiency and sales performance. Key Responsibilities1\. Internal AI Strategy \& Enablement* Partner with key functional stakeholders and technology leaders to identify and prioritize high\-impact AI use cases that improve productivity, decision quality, and time\-to\-market.
- Establish enterprise\-wide forums for knowledge sharing across functional areas.
- Create a process to prioritize internal and test initiatives, including stage\-gating, re\-use principles, and prioritization cadences.
- Establish standardized processes to track use cases, ensure resource alignment, track execution, and measure ROI.
- Identify and partner with appropriate teams to unblock challenges.
2\. Enterprise Alignment \& Change Leadership* Partner with HR, Communications, and functional leaders to drive AI literacy programs and upskilling
- Drive cultural transformation to create an AI\-first mindset across functional areas, empowering teams to use AI and analytics responsibly in daily decisions.
- Communicate complex AI and insights\-driven recommendations clearly to senior executives and cross\-functional leaders.
3\. Competitive \& Market Insights* Lead the Competitive Intelligence function, tracking market shifts, emerging disruptors, and technology trends.
- Translate competitive insights into strategic recommendations that inform corporate priorities and investment choices. Partner with Product, Marketing, and Strategy teams to ensure market learnings directly inform go\-to\-market and innovation efforts.
- Identify opportunities to leverage AI to create an “on\-demand” Competitive Intelligence capability and deeper competitor insights for business stakeholders.
4\. Customer Insights \& Experience (NPS Ownership)* Lead the Customer NPS function, driving understanding of customer sentiment, experience pain points, and improvement opportunities.
- Partner with business and functional leaders to drive measurable improvements in customer satisfaction and loyalty.
- Identify opportunities to create a customer 360 tool to provide greater customer intelligence to the organization for better data\-driven decision making.
Qualifications* 10\+ years of leadership in strategy, operations, product management or organizational transformation
- Management consulting experience strongly preferred to bring strategic rigor and structured problem\-solving into the role
- MBA or equivalent experience preferred
- Demonstrated success leading organization\-wide transformation initiatives in matrixed, global organizations
- Proven experience with AI tools and strong understanding of their impact on business processes
- Exceptional communication and stakeholder management skills, with a track record of aligning cross\-functional teams around data\-driven decisions
U.S. National Base Pay Range: $156,700 \- $290,900\. Geographic differentials may apply in some locations to better reflect local market rates.
This job is eligible for an annual incentive bonus. We know your well\-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.
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Salary Context
This $156K-$290K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Elsevier, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($223K) sits 34% above the category median. Disclosed range: $156K to $290K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Elsevier AI Hiring
Elsevier has 3 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Home, WA, US, Remote, US, Alpharetta, GA, US. Compensation range: $281K - $340K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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