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*An Important Note about Wayfair's In\-Office: Please note that this is a hybrid role based in Boston, MA and will require you to work in the office on Tuesdays, Wednesdays, and Thursdays.*
Who We Are
Global Supplier Technology (GST) builds the platforms and tools that power how suppliers do business with Wayfair and how Wayfair manages its economics at scale. Within GST, the Pricing \& Marketplace Intelligence AI/ML domain is responsible for the systems that set and optimize prices, manage unit economics, incorporate demand and forecasting signals, and leverage competitive insights to deliver compelling, profitable offers to customers.You will join a science\- and ML\-oriented product area that partners closely with Applied Scientists, Data Scientists, and ML Engineers to build models and decision systems that directly influence customer prices, supplier economics, and contribution profit.
As a Senior Product Manager for Pricing \& Marketplace Intelligence AI/ML , you will own a product area and related workstreams that use science and machine learning to optimize pricing and profitability, with a secondary focus on integrating competitor intelligence signals into commercial decisioning. You will define the strategy, roadmap, and execution plan for pricing and profit algorithms, collaborating with Engineering, Science/ML, Analytics, Finance, and commercial stakeholders to ship and iterate on products that move key business metrics. What You’ll Do
- Run discovery with stakeholders (Pricing Strategy, Category Management, Supplier Management, Promotions, Finance, etc.) and set the product vision and strategy for core pricing and profit optimization within GST.
- Translate the product vision into a sequenced roadmap across multiple workstreams, ensuring it evolves as we learn from experiments, model performance, and changing business goals.
- Partner closely with Applied Scientists and ML Engineers to design, prioritize, and launch models and decision systems that set prices, optimize contribution profit, and incorporate relevant competitor signals.
- Translate complex scientific and technical concepts into clear problem statements, product requirements, and decision frameworks that are legible to business and operations stakeholders.
- Identify, refine, and monitor KPIs and health metrics for your products to understand their impact on key business metrics (e.g., revenue, contribution profit, conversion, margin, supplier economics).
- Use Agile practices to collaborate with Engineering and Science/ML to execute on the roadmap while harnessing the power of emerging technologies.
- Ensure pricing and profit systems are observable, explainable, and trustworthy for business users by surfacing the right levers, controls, and transparency into model\-driven decisions.
- Develop and monitor OKRs for key product initiatives and ensure alignment with stakeholder objectives and Wayfair’s financial goals.
- Champion a strong team dynamic by driving cross\-functional collaboration, product quality, and team velocity, and by contributing to product craft and process improvements within GST.
- Communicate and promote the product vision, roadmap, and progress across Wayfair, including to senior and C\-level stakeholders when appropriate.
We're A Match Because You Have
- 7\+ years building software, platform, data, or algorithmic products with measurable impact, ideally in ambiguous or cross\-functional product areas.
- Demonstrated experience owning roadmaps and outcomes for products that rely heavily on data, experimentation, or algorithms (e.g., pricing, recommendations, ads, marketplace optimization, or similar).
- Strong product ownership instincts: you can move from ambiguous problem discovery to requirements, tradeoffs, adoption, and measurable outcomes.
- Strong analytical skills and comfort working with data: you define metrics, dig into large datasets with partners, and make decisions grounded in quantitative evidence.
- Proven ability to work effectively with Data Scientists and/or Applied Scientists and ML Engineers, including shaping problem formulations, success metrics, and experimentation strategies.
- Experience making data\-based conclusions and recommendations, and the ability to be hands\-on in the data analysis process (e.g., partnering closely on SQL/GBQ, experimentation readouts, and modeling insights).
- A track record of focusing on customer and business problems and creating business\-focused product solutions that balance customer value, profit, and operational complexity.
- Excellent communication and stakeholder management skills at the senior partner level; you can simplify complex technical concepts and influence diverse audiences.
- Experience working in a highly ambiguous and fast\-paced environment and driving complex, cross\-functional initiatives from inception through launch and iteration.
- Positive, people\-oriented, and collaborative attitude; demonstrated success mentoring or upskilling peers or mentees, even in an IC role.
- Experience in pricing, revenue management, marketplace dynamics, ad tech, or other AI/Economics\-heavy domains.
- Familiarity with experimentation methodologies (A/B testing) and statistical reasoning; comfort interpreting model and experiment results in partnership with Science/Analytics.
- Exposure to machine learning–driven products (e.g., having worked closely with ML teams on ranking, prediction, optimization, or decision systems).
- Experience in e\-commerce, retail, or marketplace businesses.
- Prior experience with supplier\-facing or B2B platforms is a plus.
- Bachelor’s degree in a technical or quantitative field (e.g., Computer Science, Engineering, Mathematics, Economics, Statistics) or equivalent practical experience.
Assistance for Individuals with Disabilities
Wayfair is fully committed to providing equal opportunities for all individuals, including individuals with disabilities. As part of this commitment, Wayfair will make reasonable accommodations to the known physical or mental limitations of qualified individuals with disabilities, unless doing so would impose an undue hardship on business operations. If you require a reasonable accommodation to participate in the job application or interview process, please let us know by completing our Accomodations for Applicants form.
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About Wayfair Inc.
Wayfair is one of the world’s largest online destinations for the home. Whether you work in our global headquarters in Boston, or in our warehouses or offices throughout the world, we’re reinventing the way people shop for their homes. Through our commitment to industry\-leading technology and creative problem\-solving, we are confident that Wayfair will be home to the most rewarding work of your career. If you’re looking for rapid growth, constant learning, and dynamic challenges, then you’ll find that amazing career opportunities are knocking.
No matter who you are, Wayfair is a place you can call home. We’re a community of innovators, risk\-takers, and trailblazers who celebrate our differences, and know that our unique perspectives make us stronger, smarter, and well\-positioned for success. We value and rely on the collective voices of our employees, customers, community, and suppliers to help guide us as we build a better Wayfair – and world – for all. Every voice, every perspective matters. That’s why we’re proud to be an equal opportunity employer. We do not discriminate on the basis of race, color, ethnicity, ancestry, religion, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, genetic information, or any other legally protected characteristic.
Your personal data is processed in accordance with our Candidate Privacy Notice (https://www.wayfair.com/careers/privacy). If you have any questions or wish to exercise your rights under applicable privacy and data protection laws, please contact us at dataprotectionofficer@wayfair.com.
Salary Context
This $170K-$219K range is above the median for AI Product Manager roles in our dataset (median: $188K across 140 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 3,708 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Wayfair, 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 in Demand for This Role
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 $216,175 based on 270 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($194K) sits 10% below the category median. Disclosed range: $170K to $219K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Wayfair AI Hiring
Wayfair has 1 open AI role right now. They're hiring across AI Product Manager. Based in Boston, MA, US. Compensation range: $219K - $219K.
Location Context
AI roles in Boston pay a median of $210,000 across 97 tracked positions. That's 3% below the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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