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$51.5K

$159.4K

$197K

How much do product manager personalization jobs pay per year?

As of Sep 12, 2026, the average yearly pay for product manager personalization in the United States is $159,405.00, according to ZipRecruiter salary data. Most workers in this role earn between $141,000.00 and $197,000.00 per year, depending on experience, location, and employer.

What does a product manager personalization do?

A Product Manager for Personalization is responsible for developing and optimizing products or features that deliver personalized experiences to users. They analyze user data and behavior to identify opportunities for customization, work closely with engineering and design teams to implement personalization strategies, and ensure these features align with business goals. Their role often involves prioritizing product enhancements, measuring the impact of personalization efforts, and staying updated on industry best practices. Ultimately, they aim to increase user engagement and satisfaction by making products more relevant to individual users.

What are the key skills and qualifications needed to thrive as a product manager personalization?

To thrive as a Product Manager Personalization, you need a strong background in product management, data analysis, and user experience design, often supported by a degree in business, computer science, or a related field. Familiarity with A/B testing platforms, analytics tools like Google Analytics or Mixpanel, and personalization engines is typically required. Exceptional communication, stakeholder management, and problem-solving skills help drive cross-functional collaboration and innovative solutions. These abilities are crucial for delivering personalized experiences that increase user engagement and achieve business goals.

How does a product manager personalization typically collaborate with data scientists and engineers to deliver personalized experiences?

As a Product Manager Personalization, you'll work closely with both data scientists and engineers throughout the product development lifecycle. You'll define personalization goals, prioritize features, and translate customer insights into actionable requirements. Regular meetings with data scientists help you understand model capabilities and limitations, while collaboration with engineers ensures technical feasibility and smooth implementation. Open communication and cross-functional teamwork are key to iterating quickly and delivering impactful personalized experiences for users.

What is the difference between Product Manager Personalization vs Product Manager Data Analytics?

AspectProduct Manager PersonalizationProduct Manager Data Analytics
Required SkillsUser segmentation, A/B testing, UX designData analysis, SQL, statistical methods
Work EnvironmentCross-functional teams focusing on user experienceData-driven decision making, analytics teams
Industry UsageTech, e-commerce, mediaFinance, tech, marketing

Product Manager Personalization and Product Manager Data Analytics both focus on data-driven product improvements. Personalization emphasizes tailoring user experiences through segmentation and testing, while Data Analytics centers on analyzing data to inform product decisions. Both roles require analytical skills but differ in their primary focus and methods.

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Product Manager Personalization jobs near you
Infographic showing various Product Manager Personalization job openings in the United States as of September 2026, with employment types broken down into 86% Full Time, 13% Part Time, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $159,405 per year, or $76.6 per hour.

Personalization Product Manager - Vice President

Manhattan, NY • On-site

JPMorganChase
See more jobs at JPMorganChaseexternal

Full-time

Re-posted 10 days ago

Job description

Job Summary:
JPMorganChase is a leading financial services firm dedicated to helping customers achieve their financial goals. They are seeking a Personalization Product Manager - Vice President to drive product innovation, manage the product life cycle, and deliver ML-powered personalization solutions that enhance customer experiences.
Responsibilities:
• Develops a product strategy and product vision that delivers value to customers
• Manages discovery efforts and market research to uncover customer solutions and integrate them into the product roadmap
• Owns, maintains, and develops a product backlog that enables development to support the overall strategic roadmap and value proposition
• Builds the framework and tracks the product's key success metrics such as cost, feature and functionality, risk posture, and reliability
• Define and own the strategy and roadmap for operating‑memory data products: what customer signals are needed, how they are represented, governed, and retrieved safely, and how they are delivered to the agent and domain agents at the right time
• Partner with the Channel/Interface team on how memory and context are used in the assistant experience; align on contracts, SLAs, and guardrails to ensure reliability and safe use
• Lead the development of ML‑powered personalization and next‑best‑action capabilities for marketing and servicing; frame hypotheses, prioritize use cases, and measure impact through experimentation
• Translate user research and analytics into epics, user stories, and acceptance criteria; manage the product backlog and delivery across discovery, launch, and continuous improvement
• Collaborate closely with Data Scientists and ML Engineers across the model lifecycle (design, training, evaluation, deployment, monitoring) for LLM/ML‑based solutions
• Champion an API‑ and event‑driven architecture on cloud infrastructure to ensure scalable, reliable delivery of context and signals across channels. Establish and track product KPIs for engagement, quality, and business outcomes; ensure delivery against time, cost, and quality targets
• Uphold responsible AI and data practices in partnership with risk, privacy, and compliance teams, including consent management, and fairness to provide people leadership, mentor product managers, foster a culture of experimentation and measurable outcomes, and influence cross‑functional partners
Qualifications:
Required:
• 5+ years of experience or equivalent expertise in product management or a relevant domain area
• Advanced knowledge of the product development life cycle, design, and data analytics
• Proven ability to lead product life cycle activities including discovery, ideation, strategic development, requirements definition, and value management
• Proven product management leadership delivering AI‑powered products to production in customer‑facing environments, in close partnership with Data Scientists and ML Engineers across the model lifecycle
• Experience defining and shipping data products that support agentic assistants (e.g., operating memory, context, or signals) and integrating them with partner teams that own the channel experience
• Demonstrated success with ML‑driven personalization and next‑best‑action for marketing and/or servicing, including experimentation (e.g., A/B testing) and outcome measurement
• Proficient knowledge of the product development life cycle, including discovery, requirements definition, and backlog management (epics, user stories, refinement, PBR, JIRA)
• Strong data literacy and the ability to turn user research, journey insights, and product metrics into decisions and roadmaps that deliver on time, cost, and quality
• Excellent people leadership and stakeholder management: mentoring product managers and influencing partners across Product, Design, Engineering, Marketing/Servicing, and business teams
• Experience with API‑first delivery on cloud (e.g., AWS) and coordination across multi‑channel experiences (mobile, web, contact center, branch, marketing). Clear, structured communicator with strong written and presentation skills
Preferred:
• Demonstrated prior experience working in a highly matrixed, complex organization
• Hands‑on experience with AI/ML and LLMs in production (e.g., agentic assistants, personalization, recommendations), including model evaluation and iteration with Data Science partners
• Experience with conversational/agentic systems, operating‑memory or context architectures, and recommendation systems for marketing/servicing
• BS or MS in Engineering, Data Science, or a comparable field of study
• Knowledge of current digital banking trends and customer experience patterns, and familiarity with privacy, consent, and fairness considerations in AI
Company:
JPMorganChase is a financial services company providing banking, investment, financial management, and related services. It is a sub-organization of PricingDirect. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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