Senior Product Manager, Fictiv

• 5+ years of product management experience in B2B, SaaS, or technology-driven industries
• Demonstrated experience developing AI-enabled products or working directly with AI research or data science teams to bring intelligent features to market
• Strong analytical and strategic skills — able to balance visionary thinking with operational precision
• Expertise in Agile product development, roadmap ownership, and cross-functional execution
• Experience managing products that integrate multiple systems (ERP, MES, or supply chain networks)
• Proven ability to influence and collaborate across distributed teams and time zones

Desirable

• As a Senior Product Manager, you’ll play a defining role in building the next-generation Operating System for New Product Development (NPD) — one that leverages artificial intelligence, automation, and deep manufacturing intelligence to revolutionize how products are designed, sourced, and delivered globally
• You will own and evolve Fictiv’s Manufacturing Quoting & Fulfillment platform, the intelligent backbone that powers how our customers, operations teams, and global manufacturing partners collaborate
• Your work will extend beyond traditional product management — you’ll define, prioritize, and launch innovative technologies that dramatically increase accuracy, speed, and efficiency across quoting, fulfillment, and supplier orchestration
• You’ll partner with our advanced technology R&D team to turn cutting-edge research into production-ready solutions, bringing automation, predictive analytics, and generative intelligence into every step of the manufacturing lifecycle
• Define the long-term vision for Fictiv’s manufacturing intelligence platform, emphasizing how AI and automation deliver measurable value for customers and partners
• Identify strategic opportunities to integrate machine learning models, natural language understanding, and computer vision into quoting, routing, and supplier selection workflows
• Build business cases for AI investments, articulating ROI through improved quote accuracy, reduced turnaround time, and increased partner throughput
• Partner with data science and R&D leaders to shape Fictiv’s roadmap for AI-enabled quoting, intelligent fulfillment, and predictive supply orchestration
• Collaborate closely with AI research and data engineering teams to translate complex models into usable, scalable product features
• Define product requirements for systems that leverage large language models (LLMs), predictive algorithms, and generative tools to extract, classify, and structure manufacturing data (from drawings, CAD files, and natural language requirements)
• Create feedback loops between AI systems and operational users to continuously improve model performance and reliability
• Lead experiments and pilot programs that validate new AI capabilities before wide-scale deployment
• Translate strategic initiatives into clear, actionable roadmaps with measurable outcomes
• Establish frameworks to track input/output metrics, quantifying how new AI and automation features improve key business KPIs (margin, lead time, throughput, etc.)
• Prioritize trade-offs between innovation speed and reliability, ensuring that AI-based systems meet the performance standards expected of mission-critical manufacturing infrastructure
• Partner with distributed engineering teams in the US, India, to ensure consistent execution and iteration velocity
• Deeply understand the workflows, motivations, and friction points of engineers, procurement teams, and manufacturing partners
• Use a data-driven and empathic approach to discover high-impact opportunities for AI intervention — from automated feature recognition to supplier recommendation models
• Drive programs that integrate human feedback into AI systems, ensuring transparency, explainability, and trust
• Build strong relationships with operations, design, and go-to-market teams to ensure AI-driven innovations translate into real business outcomes
• Communicate complex technical concepts simply, securing buy-in across executive, engineering, and commercial teams
• Champion a culture of experimentation, where data, user feedback, and model performance insights continuously inform product evolution