Technical Product Manager, Vice President
Listed on 2026-01-04
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IT/Tech
AI Engineer, Data Analyst
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 Vice President 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 n us to make a meaningful impact by delivering robust, high-quality tools that empower our users and advance our platform’s capabilities.
Job 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
- 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.
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