What are the responsibilities and job description for the Product Manager Platform Experimentation position at JPMorgan Chase?
You enjoy shaping the future of product innovation as a core leader, driving value for customers, guiding successful launches, and exceeding expectations. Join our dynamic team and make a meaningful impact by delivering high-quality products that resonate with clients.
As a Technical Product Manager in Digital Channels - Experimentation, you are an integral part of the team that innovates new product offerings and leads the end-to-end product life cycle. As a core leader, you are responsible for acting as the voice of the customer and developing profitable products that provide customer value. Utilizing your deep understanding of how to get a product off the ground, you guide the successful launch of products, gather crucial feedback, and ensure top-tier client experiences. With a strong commitment to scalability, resiliency, and stability, you collaborate closely with cross-functional teams to deliver high-quality products that exceed customer expectations.
As a key member of our team, you’ll have the opportunity to rapidly prototype new features, validate complex statistical engines, and help shape the future of experimentation at Chase. Join us to make a meaningful impact by delivering robust, high-quality tools that empower our users and advance our platform’s capabilities.
- 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
- Act as the technical thought leader in advancing statistical methods and GenAI capabilities of the platform
- Rapidly build prototypes of new features and concepts for the experimentation platform to enhance self-serviceability using Python or similar languages.
- Debug, validate, and optimize statistical methods to ensure accuracy and reliability of experiment results.
- Triage production issues related to experimentation with partners to identify root cause.
- Collaborate with product, design, and engineering teams to refine technical requirements and scope.
- Stay current with best practices in experimentation, statistics, and analytics, and apply them to platform development.
- 5 years of experience or equivalent expertise in product management, data science, computer science 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
- Practical coding ability (e.g., Python, scripting, or similar)—able to read, understand, and write code to support prototyping, troubleshooting, and integration.
- Strong background in statistical methods for A/B Testing.
- Strong proficiency in Python, SQL, Snowflake, with the ability to create complex queries and analyses.
- Excellent problem-solving skills and attention to detail.
- Strong communication skills, with the ability to explain complex analytics concepts to non-technical users.
- Ability to work independently and collaboratively in a fast-paced, dynamic environment.
- Proven ability to collaborate and achieve actionable results with cross-functional teams.
- Demonstrated prior experience working in a highly matrixed, complex organization
- Experience in bringing GenAI features to production at scale
- Prior experience working in a highly matrixed, complex organization
- Background in data science, data engineering, or software engineering
- Experience in scaling platforms
- Advanced statistical knowledge related to experimentation, including Contextual Multi-Armed Bandit (CMAB), Sequential Testing, CUPED (Controlled Experiments Using Pre-Experiment Data), and other advanced methodologies.