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Job Description:
We believe in bold ideas, diverse perspectives, and the drive to transform knowledge into impact. Here, your curiosity fuels progress, your voice shapes innovation, and your ambition helps redefine what’s possible within science and learning. We are a culture that obsesses over impact, challenges, and drives what’s next to power infinite possibilities for our customers, colleagues and society at large.
About the Role:
As Wiley builds out its AI and Data Analytics marketing organisation, this role sits at the centre of that effort \- responsible for ensuring that what Wiley builds, how it is positioned, and how it is sold is grounded in rigorous market understanding and genuine customer insight.
The Senior Director of Product Marketing \& Customer Insights is both a strategic architect and a commercial operator: setting the narrative for a growing portfolio of AI and data products, owning the research infrastructure that informs product and go\-to\-market decisions, and equipping commercial teams to compete and win in enterprise and R\&D\-intensive markets.
This is a role for a senior leader who can operate with credibility across Product, Sales, and Executive stakeholders; who understands the complexity of selling AI into large organisations; and who brings the discipline to turn market intelligence and customer voice into decisions, not just deliverables.
Key Responsibilities
*Product Marketing*
- Develop and own compelling value propositions, messaging frameworks, and positioning for Wiley's AI\&DA product portfolio — differentiated by customer segment, buying role, and customer maturity stage. Ensure Wiley's positioning is credibly differentiated in a crowded and heavily hyped market, with a clear narrative that cuts through AI market noise.
- Lead go\-to\-market strategy and execution for new product launches, feature releases, and entry into new verticals — working in close partnership with Product, Sales, and the broader marketing organisation. Develop strategies that address the full commercial journey, including converting trials, pilots, and proof\-of\-concept engagements into contracted revenue.
- Provide strategic input into pricing, packaging, and bundling decisions alongside Product and Commercial leadership, ensuring that how Wiley's AI \&DA products are structured for market reflects both customer buying behaviour and competitive dynamics.
- Oversee the b uild and maintain enance of a suite of sales enablement materials — including pitch decks, competitive battle cards, solution briefs, technical whitepapers, and ROI and business case tools \- that equip the sales team to win across enterprise, corporate R\&D verticals.
- Partner with Demand Generation and Content teams to ensure that campaigns are grounded in strong product and market insight. Lead the development of industry analyst engagement (Gartner, Forrester, IDC), and vertical\-specific narratives that establish Wiley's credibility and authority in AI and data for R\&D\-intensive industries.
- Define and track key product marketing metrics including pipeline contribution, win/loss trends, sales enablement adoption, pilot\-to\-contract conversion rates, and category awareness and share of voice in target markets.
*Customer Insights*
- Oversee the Voice of Customer ( VoC ) programme across the customer lifecycle – ensuring the de livery of structured research (interviews, surveys, advisory sessions) that surfaces what customers need, how they buy, and where existing products fall short. Ensure these insights are synthesised and consistently operationalised into product, commercial, and marketing decisions rather than filed as reports.
- IN partnership with Commercial and Product teams, establish and manage Customer Advisory Board s (CAB s ) represe nting Wiley's most strategic AI and data customers. Use th ese forum s to validate product direction, stress\-test positioning, and build commercial relationships at senior levels.
- Own win/loss analysis as a formal research function — going beyond anecdotal sales feedback to identify structural patterns in why Wiley wins or loses, and translating those findings into actionable recommendations for Product, Sales, and Marketing leadership.
- Commission and synthesise primary and secondary market research to size addressable markets, identify whitespace, and understand where AI and data analytics spend is flowing in target verticals including pharma, biotech, materials science, and corporate R\&D functions.
- Develop deep buyer and user personas grounded in original research — covering motivations, decision criteria, pain points, and the internal political dynamics that shape how AI and data investments are approved and implemented in enterprise organisations.
*Market Intelligence \& Competitive Strategy*
- O wn a systematic competitive intelligence capability covering the AI and data analytics landscape . Ensure that intelligence is current, structured, and actively used to sharpen positioning and sales strategy rather than sitting in a slide deck.
- Monitor market trends, regulatory developments, and shifts in customer sentiment that have implications for Wiley's product strategy and go\-to\-market approach.
- Serve as the internal authority on the competitive landscape — providing regular briefings to Product and Sales and acting as a key input into product roadmap prioritisation.
*Leadership \& Cross\-functional Influence*
- Build, lead, and develop a high\-performing AI\&DA product marketing and customer insights team, establishing clear roles, ways of working, and a culture of commercial rigour and customer empathy.
- Act as a senior voice of the customer and market within Wiley's AI and Data Analytics leadership \- ensuring that product development, commercial strategy, and marketing investment are grounded in external reality rather than internal assumption.
Required Qualifications:
Master's degree in Marketing , Business, or related field; MBA required
15\+ years of progressive product marketing leadership with 8\+ years in senior executive roles
Proven track record of leading large product marketing organizations and achieving exceptional revenue growth
Executive\-level strategic thinking and advanced business leadership capabilities
Exceptional communication, presentation, and executive presence
Extensive experience collaborating with C\-level executives and board\-level stakeholders
Advanced expertise in enterprise product marketing strategy, market leadership, and revenue optimization
Proven ability to drive large\-scale organizational transformation and lead through significant change
Strong financial leadership and experience managing substantial product marketing investments
Expert knowledge of advanced market research, competitive intelligence, and strategic product development
Demonstrated success in building and leading high\-performing senior executive teams
Global market expertise and international business development experience
We power infinite possibilities.
For more than 200 years, we've transformed knowledge into discoveries that shape the world. Today, our global team of innovators, creators, and experts is driving what's next in science, education, and publishing—creating impact that reaches everywhere.
We're not just observers of progress. We're the ones accelerating scientific breakthroughs, advancing learning, and sparking innovation that redefines entire fields and improves lives.
Here, your talent matters. Your ideas have room to grow. And your work creates breakthroughs that can change everything.
Wiley is an equal opportunity/affirmative action employer. We evaluate all qualified applicants and treat all qualified applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, disability, protected veteran status, genetic information, or based on any individual's status in any group or class protected by applicable federal, state or local laws. Wiley is also committed to providing reasonable accommodation to applicants and employees with disabilities. Applicants who require accommodation to participate in the job application process may contact [email protected] for assistance.
We are proud that our workplace promotes continual learning and internal mobility. Our values support courageous teammates, needle movers, and learning champions all while striving to support the health and well\-being of all employees. We offer meeting\-free Friday afternoons allowing more time for heads down work and professional development, and through a robust body of employee programing we facilitate a wide range of opportunities to foster community, learn, and grow.
We are committed to fair, transparent pay, and we strive to provide competitive compensation in addition to a comprehensive benefits package. The range below represents Wiley's good faith and reasonable estimate of the base pay for this role at the time of posting roles either in the United Kingdom, Canada or USA. It is anticipated that most qualified candidates will fall within the range, however the ultimate salary offered for this role may be higher or lower and will be set based on a variety of non\-discriminatory factors, including but not limited to, geographic location, skills, and competencies.
When applying, please attach your resume/CV to be considered.
Salary Range:
160,500 USD to 236,233 USD\&\#xa;\&\#xa;\#LI\-JL1
Salary Context
This $160K-$236K range is above 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 Wiley, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($198K) sits 9% above the category median. Disclosed range: $160K to $236K.
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.
Wiley AI Hiring
Wiley has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Hoboken, NJ, US. Compensation range: $236K - $236K.
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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