Machine Learning and AI Opportunities

About the role

SharkNinja is a global product design and technology company, with a diversified portfolio of 5-star rated lifestyle solutions that positively impact people's lives in homes around the world. Powered by two trusted, global brands, Shark and Ninja , the company has a proven track record of bringing disruptive innovation to market and developing one consumer product after another has allowed SharkNinja to enter multiple product categories, driving significant growth and market share gains. Headquartered in Needham, Massachusetts with more than 4,100 associates, the company's products are sold at key retailers, online and offline, and through distributors around the world. AI at SharkNinja At SharkNinja, we're building an AI-native culture. We're not waiting for the future; we're creating it. Our people are expected to experiment boldly, adopt new tools, and continuously raise what's possible to create meaningful impact for our consumers. If you believe the best way to do your job hasn't been invented yet, you'll fit right in. Note: This is a Pipeline Opening and Not Tied to A Specific Opening The AI Product Manager owns strategy, roadmap, and delivery for AI initiatives across the enterprise. This role translates business objectives into a clear prioritized backlog, collaborating with engineering teams and external partners to deliver impactful solutions that drive measurable business impact through revenue growth or new operational efficiencies. The Product Manager works closely with Global Data Product Management, including Data and MDM, to ensure AI products are built on trusted, standardized data foundations. The role requires strong judgment in application integrations and build vs. buy tradeoffs, balancing speed, cost, and enterprise standards.

Responsibilities

• Area
• Product Strategy & Vision
• Define a 3-9-month AI roadmap aligned to company strategic goals, with clear problem statements and value hypotheses to maximize value.
• Portfolio & Roadmap
• Convert strategy into prioritized increments with PRDs, acceptance criteria, and release plans; keep a transparent backlog.
• Evaluation & Experimentation
• Own evaluation for AI features: offline tests, human and automated evals, and online A/B experiments to optimize quality, latency, and cost effectiveness. Publish decision logs for model, prompt, and dataset changes.
• Responsible AI & Guardrails
• Define safety, privacy, and compliance requirements; implement guardrails and review rituals with Security and Legal; ensure traceability of data, prompts, and outputs.
• Application Integrations
• Define product requirements for API-first and event driven integrations across CRM/ERP/eCommerce and data platforms; align on data contracts, SLAs, auth/PII handling, and system observability with Platform teams.
• Build vs Buy
• Lead structured tradeoff analyses (TTV, TCO, vendor lock in, differentiation, compliance). Run proofs of value with vendors when needed and recommend paths forward, highlighting risks and contingency plans..
• Cross‑Functional Leadership
• Lead squads spanning ML Engineering, MLOps, Data Engineering, Analytics, and Business stakeholders; keep scope, risks, and dependencies visible.
• Impact Measurement
• Define clear KPIs that connect business outcomes to product performance, and provide executives with simple, actionable reporting against those targets.
• Partnerships
• Collaborate with Director, ML and AI, Security, Legal, Procurement, and Global Data Product Management (Data and MDM) to align standards, governance, and delivery.
• Required Qualifications
• 5+ years in product management with shipped data or AI features tied to measurable outcomes.
• Proven ownership of evaluation and experimentation for AI features (offline metrics, human/auto evals, and A/B testing).
• Handson experiences driving application integrations at enterprise scale (APIled and iPaaS patterns, SLAs, data contracts, identity).
• Demonstrated ability to lead build vs. buy decisions, supported by clear financial models and risk analyses.
• Excellent executive communication and stakeholder leadership.

Requirements

• Experience optimizing Amazon sales and ad channels.
• Experience with Salesforce Cloud and CDP integrations.
• Exposure to LLMOps practices (prompt versioning, guardrails, eval frameworks), vector search/RAG, or model observability.
• Success Metrics (first 12 months)
• AI roadmap approved and in execution within 90 days.At least two AI product increments shipped with adoption targets met.
• Application integrations delivered on time with measurable data quality and reliability improvements.
• At least two major technology decisions completed with a formal build vs. Buy analysis and exec approval.
• AI initiatives deliver a minimum 5x ROI on Capex investments, contributing directly to revenue growth.
• Salary and Other Compensation: The annual salary range for this position is displayed below. Factors which may affect starting pay wit

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