Sr. Director - AI Product Development & Product Excellence Transformation

SharkNinja•Needham, MA

5d•Onsite

About The Position

SharkNinja is a global product design and technology company focused on creating lifestyle solutions. The company is building an AI-native culture and encourages experimentation and adoption of new tools to create meaningful consumer impact. This role is a high-impact leadership position at the intersection of AI, product development, product quality, and organizational transformation. The Sr. Director will own the strategy and execution of AI integration across SharkNinja's Product Development (PD) and Product Excellence (PE) organizations, covering the entire product lifecycle. The current processes rely heavily on manual methods, and the role aims to transform these by building a data foundation, deploying AI capabilities, and optimizing workflows for faster, smarter product development with higher quality. The position has a dual mandate: drive current AI initiatives to completion and shape future AI capabilities. The role reports to the VP, Chief of Staff to the CEO, with a dotted line to the Chief Product Officer and EVP, Product Excellence. The Sr. Director will lead a team of AI Fellows and drive change management across global workforces with varying AI fluency. Success will be measured by adoption and measurable outcomes, fostering a culture of experimentation.

Requirements

10+ years of professional experience in AI/ML, product development leadership, program management, data architecture, quality/product excellence, or technology transformation roles.
Strong technical fluency in AI/ML and data infrastructure.
Proven track record leading large-scale transformation or change management initiatives, ideally in product development, R&D, or consumer products environments.
Deep understanding of product development lifecycles in a hardware or consumer electronics context, including stage-gate processes, EV/PV builds, and cross-functional launch execution.
Proven ability to both build (hands-on implementation) and think strategically.
Strong business acumen: connect technical capabilities to business outcomes and communicate this to non-technical stakeholders.
Exceptional leadership skills: attract talent, build high-performing teams, and influence without authority across a matrixed organization.
Decisive and action-oriented: make decisions with imperfect information, move fast, and course-correct without hesitation.
Comfortable operating in ambiguity and a high-velocity environment where priorities shift and speed matters more than perfection.
Travel required (domestic and international, including China manufacturing sites).

Nice To Haves

Experience at an AI-native company or within a product organization where AI was core to the development process.
Background bridging technical build teams and business stakeholders, with the ability to translate requirements in both directions.
Experience productionalizing AI prototypes (moving from proof-of-concept to enterprise-grade tools with proper back-end infrastructure).
Familiarity with quality engineering concepts: FMEAs, DVF, reliability testing, voice-of-consumer analysis, return rate analytics.

Responsibilities

Architect and implement a unified data infrastructure across PD and PE to resolve data fragmentation and silos.
Map data assets across PD and PE and build ingestion pipelines for AI systems.
Partner with IT and data engineering to connect PD and PE data infrastructure to the enterprise data architecture.
Define and execute the AI transformation roadmap across the full PD lifecycle, identifying new AI opportunities.
Lead the reimagination of program management infrastructure, replacing manual workflows with AI-powered tooling.
Deploy AI-powered planning intelligence to improve forecasting accuracy and reduce surprises.
Transform consumer insights capabilities, including rating prediction and voice-of-consumer analysis.
Define the next generation of AI-powered quality with PE leadership.
Reimagine product integrity workflows by making lessons learned, test results, and FMEAs accessible through AI.
Strengthen PE's contribution to PRDs by building AI systems that surface relevant historical data.
Build, lead, and scale a team of AI Fellows embedded directly into PD and PE teams.
Drive change management across large, global workforces with varying levels of AI fluency.
Translate complex AI capabilities into practical, adoptable solutions for product and engineering teams.
Establish success metrics, track adoption, and report measurable outcomes to executive leadership.
Champion a culture of experimentation: fast iteration, learning from failure, and scaling what works.