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About This Role
Are you passionate about using AI tools?
Do you enjoy collaborating cross\-functionally to deliver on common goals?
About our Team
LexisNexis Legal \& Professional, which serves customers in more than 150 countries with 11,300 employees worldwide, is part of RELX, a global provider of information\-based analytics and decision tools for professional and business customers.
About the Role
LexisNexis is transforming how law firms and legal teams deliver work, and we are looking for exceptional former AmLaw firm attorneys to help lead that change.
The Strategic Business Development Lead (New Business Focus) is a senior, consultative, customer\-facing role that operates alongside our sales professionals to originate, shape, and progress complex enterprise opportunities. You will build trusted relationships with senior law firm stakeholders, conduct in\-depth discovery, and deliver tailored demonstrations and insights to help our sales team successfully generate new opportunities and demonstrate the value of our services to prospective customers.
Responsibilities
- Partnering with Account Executives to research prospects, shape pursuit strategies, and drive opportunities from initial outreach through close, including gaining first meetings, positioning, objection handling, and driving stakeholder alignment.
- Engaging managing partners, practice chairs, and firm leadership to understand priorities and position LexisNexis as a trusted partner in legal AI and workflow transformation.
- Leading senior\-level discovery and structured workflow analysis to diagnose challenges, map decision makers / champions, and identify value levers, inefficiencies, and measurable ROI opportunities.
- Translating legal workflows into clear, commercially compelling use cases and solution strategies, aligned to practice areas and firm strategic objectives.
- Designing and delivering tailored, insight\-led demonstrations and high\-impact presentations that connect workflow insight, product capability, and commercial outcomes across 1:1, small group, and large group settings.
- Developing bespoke proof\-of\-concept engagements to validate value and build confidence; serve as the Voice of the Customer by partnering with product/engineering/marketing/strategy, informed by customer/competitor/market research, and represent LexisNexis at industry events and executive briefings.
Requirements
- Have a JD with 3\+ years of experience practicing law at an AmLaw 50 law firm
- Have a background in corporate, finance, disputes/litigation, or other complex practice areas with demonstrable client\-facing experience (preferred).
- Be able to operate effectively in a target\-driven, revenue\-accountable role with a clear hunter mindset (pipeline/revenue ownership) to win new logos
- Have experience establishing credibility with partners, firm leadership, and senior in\-house counsel through strong executive presence, legal expertise, and AI fluency.
- Be able to deliver persuasive, insight\-led presentations and demonstrations that engage both legal and business stakeholders (including impromptu whiteboard\-style and formal presentations/demos).
- Demonstrate a deep understanding of legal workflows, legal service delivery models, and emerging legal technology trends \- able to translate this into tailored, workflow\-specific value narratives.
- Demonstrate curiosity, intellectual enthusiasm, and conviction about how AI and legal technology can transform legal workflows and client delivery.
- Have the ability to collaborate effectively across teams (sales, marketing, product, client success) to deliver unified client engagement strategies. Sales or customer\-facing experience (including law firm business development, secondment, or experience directly managing law firm client matters/relationships) is a plus.
Success Metrics
- Meetings booked and opportunities created
- Revenue closed
- Conversion cycle progression, effective at moving prospects towards signing a deal
Work in a way that works for you
We promote a healthy work/life balance across the organization. We offer an appealing working prospect for our people. With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long\-term goals.
About the Business
LexisNexis Legal \& Professional® provides legal, regulatory, and business information and analytics that help customers increase their productivity, improve decision\-making, achieve better outcomes, and advance the rule of law around the world. As a digital pioneer, the company was the first to bring legal and business information online with its Lexis® and Nexis® services.
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U.S. National Base Pay Range: $108,400 \- $201,500\. Total Target Cash Range: $166,800 \- $309,700\. Geographic differentials may apply in some locations to better reflect local market rates. If performed in Illinois, the base pay range is $113,800 \- $211,500, the total target cash range is $175,100 \- $325,200\.If performed in Chicago, IL, the base pay range is $119,200 \- $221,600, the total target cash range is $183,500 \- $340,700\.If performed in New York, the base pay range is $119,200 \- $221,600, the total target cash range is $183,500 \- $340,700\.If performed in New York City, the base pay range is $130,200 \- $241,700, the total target cash range is $200,100 \- $371,600\.If performed in Rochester, NY, the base pay range is $108,400 \- $201,500, the total target cash range is $166,800 \- $309,700\.Pay mix between base and variable pay varies based on sales role; please discuss with the recruiter.
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 $108K-$340K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $174K across 475 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 26,159 AI roles we're tracking, AI Product Manager positions make up 2% of the market. At Elsevier, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $204,600 based on 532 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($224K) sits 10% above the category median. Disclosed range: $108K to $340K.
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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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