Sr. Director - AI Product Development & Product Excellence Transformation

SharkNinja•Needham, MA

5d•$225,000 - $275,000•Onsite

About The Position

This is a high-impact leadership role at the intersection of AI, product development, product quality, and organizational transformation. You will own the strategy and execution of AI integration across both SharkNinja's Product Development (PD) and Product Excellence (PE) organizations -- spanning the full product lifecycle from category strategy and conception through launch, and extending into consumer insights, product integrity, process quality, and manufacturing engineering. Today, much of this work relies on PowerPoint-based program management, fragmented data, manual analysis, and institutional knowledge trapped in people's heads. You will change that. You will build the data foundation, deploy AI capabilities, and transform workflows so that SharkNinja develops products faster, smarter, and with higher quality at every stage. This role carries a dual mandate: drive the current portfolio of AI initiatives to measurable completion, and partner directly with PD and PE leadership to shape what comes next. You are not here to deliver a fixed list of projects. You are here to build the AI capability that transforms how these organizations work -- today and in the future. You report directly to the VP, Chief of Staff to the CEO, with a dotted line to both the Chief Product Officer and the EVP of Product Excellence.

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: you can evaluate tools, assess platforms, understand data pipelines, and hold your own in technical discussions with engineers, data scientists, and product teams
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 -- not just one or the other
Strong business acumen: you connect technical capabilities to business outcomes and communicate that connection to non-technical stakeholders
Exceptional leadership skills: you attract talent, build high-performing teams, and influence without authority across a matrixed organization
Decisive and action-oriented: you 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

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, resolving the fragmented, siloed data landscape that is the #1 bottleneck to AI adoption
Map data assets across both organizations (historical product development data, conception decks, sprint timelines, EV/PV outcomes, test results, FMEAs, lessons learned, consumer feedback, return data, quality metrics) and build the ingestion pipelines that make this data usable by AI systems
Partner with IT and data engineering to connect PD and PE data infrastructure to the enterprise data architecture (Snowflake, AWS)
Define and execute the AI transformation roadmap across the full PD lifecycle, while continuously identifying new AI opportunities with PD leadership as capabilities evolve
Lead the reimagination of program management infrastructure, replacing manual, PowerPoint-based workflows with AI-powered tooling that enables real-time product status visibility, automated accountability, and permission-based dashboards
Deploy AI-powered planning intelligence by ingesting historical product development data to improve forecasting accuracy and reduce late-stage surprises
Transform consumer insights capabilities -- 5-star rating prediction, high rate of sale forecasting, voice-of-consumer analysis at scale -- and partner with PE leadership to define what the next generation of AI-powered quality looks like
Reimagine product integrity workflows by making lessons learned, test results, and FMEAs accessible through AI, so past product knowledge automatically informs future product development
Strengthen PE's contribution to PRDs by building AI systems that surface relevant historical data (prior test failures, consumer complaints, quality patterns) during the requirements process
Build, lead, and scale a team of AI Fellows embedded directly into PD and PE teams to drive hands-on AI adoption
Drive change management across large, global workforces with varying levels of AI fluency, with particular focus on director-level and below adoption
Translate complex AI capabilities into practical, adoptable solutions that product managers, program managers, quality engineers, and cross-functional teams actually use
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