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Senior Product Associate - Payments

JPMorgan Chase

Senior Product Associate - Payments

JPMorgan Chase

New York, NY, United States, US

·

On-site

·

Full-time

·

3w ago

Required skills

Go

Unlease your expertise in product development and optimization by leveraging user research, analyzing metrics, and collaborating across one of the world’s most innovative financial organizations.

As a Senior Product Associate in Payments Data & Analytics, you contribute to the team by leveraging your expertise in product development and optimization to make a significant impact, supported by user research and customer feedback to fuel the creation of innovative products and continuously improve existing offerings. Collaborate closely with cross-functional teams and play a crucial role in shaping the future of our products and ongoing success.

You lead the development of business analytics solutions for Merchant Services internal users—and potentially clients—driving measurable efficiency and better client outcomes across the Payments organization. You are passionate about building and scaling AI/ML products, with a track record of driving adoption from concept through launch and continuous improvement.

Job responsibilities

  • Partners with the Product Manager to identify new product opportunities that reflect the needs of our customers and the market through user research and discovery

  • Considers and plans for upstream and downstream implications of new product features on the overall product experience

  • Supports the collection of user research, journey mapping, and market analysis to inform the strategic product roadmap and provide insight on potential product features that provide value to customers

  • Analyzes, tracks, and evaluates product metrics including work to time, cost, and quality targets across the product development life cycle

  • Writes the requirements, epics, and user stories to support product development

  • Lead and manage product lifecycle for AI/ML-driven analytics products from discovery through delivery, including vision, roadmap, requirements, go-to-market, and post-launch optimization.

  • Partner with Data Science to shape model use cases, features, and performance metrics; ensure explainability, model monitoring, and responsible AI guardrails are embedded.

  • Collaborate with Engineering/Architecture to define data platform capabilities and manage daily stand ups adopting Agile (write detailed product requirements, data pipelines, governance, lineage, metadata, MLOps, APIs) and ensure scalable, secure delivery.

  • Establish product KPIs/OKRs and analytics instrumentation to measure usage, satisfaction, and business impact; run experiments and A/B tests to improve engagement. Optimize adoption to drive desired business outcomes.

  • Orchestrate cross-functional delivery ceremonies and communications (standups, sprint reviews, stakeholder readouts); remove blockers and manage dependencies. Champion user experience by conducting discovery interviews, usability tests, and feedback loops with Merchant Services internal users and client stakeholders.

  • Ensure compliance with data privacy, security, and model risk management standards; partner with controls and legal partners to meet firmwide policies.

  • Maintain a deep understanding of the AI/ML and data platform landscape, including vendor capabilities, open-source tools, and emerging best practices Innovate and come up with new ways to drive efficiency in the Merchant Services Organization

Required qualifications, capabilities, and skills

  • 3+ years of experience or equivalent expertise in product management or a relevant domain area
  • Proficient knowledge of the product development life cycle
  • Experience in product life cycle activities including discovery and requirements definition
  • Developing knowledge of data analytics and data literacy
  • Experience working with cross-functional teams in Agile environments.
  • Excellent communication skills with the ability to articulate complex technical topics to non-technical audiences and build consensus across diverse stakeholders
  • Management consulting skillset (problem solving, impact orientation, business understanding, program management, executive presence, presentation skills)

Preferred qualifications, capabilities, and skills

  • Experience in data analytics (e.g., SQL or Python) – candidate needs to have the ability to analyze data to make decisions
  • Related experience delivering data, analytics, or AI/ML products at scale.
  • Bachelor’s degree in a quantitative, technical, or business discipline, Masters in math, analytics or Business is preferred
  • Demonstrated experience with enterprise data platforms, analytics tooling, and/or MLOps workflows (e.g., feature stores, model deployment, monitoring)
  • Familiarity with AI/ML concepts (supervised/unsupervised learning, evaluation metrics, drift, explainability) and translating them into product features and controls

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About JPMorgan Chase

JPMorgan Chase

JPMorgan Chase & Co. is an American multinational banking institution headquartered in New York City and incorporated in Delaware. It is the largest bank in the United States, and the world's largest bank by market capitalization as of 2025.

300,000+

Employees

New York City

Headquarters

$500B

Valuation

Reviews

4.2

10 reviews

Work-life balance

4.2

Compensation

4.3

Culture

4.5

Career

4.4

Management

4.1

75%

Recommend to a friend

Pros

Good pay and benefits

Work-life balance

Career advancement opportunities

Cons

Heavy workload at times

Career advancement takes time

Pay could be better in some roles

Salary Ranges

55 data points

Junior/L3

Mid/L4

Senior/L5

Junior/L3 · Analyst

21 reports

$126,500

total per year

Base

$110,000

Stock

-

Bonus

-

$95,450

$155,250

Interview experience

4 interviews

Difficulty

2.8

/ 5

Duration

14-28 weeks

Interview process

1

Application Review

2

HireVue Video Interview

3

Technical/Behavioral Assessment

4

Final Interview Round

5

Offer Decision

Common questions

Behavioral/STAR

Technical Knowledge

Past Experience

Culture Fit

Case Study