Sr. Director - AI Product Development & Product Excellence Transformation

Sr. Director - AI Product Development & Product Excellence Transformation

Location: Boston, MA preferred;
New York, NY acceptable (On-site)

Reports to: VP, Chief of Staff to the CEO

Dotted Line: Chief Product Officer and EVP, Product Excellence

Direct Reports: AI Transformation Team (AI Fellows)

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 Shark Ninja'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 Shark Ninja 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.

• 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 work forces 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…