Product Manager – Internal AI Enablement

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November 19

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Stord

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SaaS • Transport • eCommerce

Stord is a cloud supply-chain company that combines fulfillment network operations with integrated SaaS (OMS/WMS) to power omnichannel and DTC e-commerce brands. It provides warehousing, last-mile and transportation services, order routing, inventory management, and consumer-facing delivery tools to improve conversion, speed up fulfillment, and protect brand experience. Stord serves growing B2B and direct-to-consumer brands across food & beverage, beauty, apparel and other retail categories, offering a customizable fulfillment network and software platform to optimize costs and delivery performance.

1001 - 5000 employees

Founded 2015

☁️ SaaS

🚗 Transport

🛍️ eCommerce

📋 Description

• Partner with leaders across Finance, People, Operations, and Business teams to identify pain points where AI can drive measurable impact • Quantify the business case for AI initiatives in terms of cost savings, efficiency gains, and margin improvement • Build and maintain a prioritized roadmap of internal AI initiatives based on ROI and strategic importance • Track and report on the business impact of deployed AI solutions • Define requirements for internal AI tools and automation across back-office functions • Work with data scientists and ML engineers to develop and deploy AI models that solve real operational problems • Collaborate with internal stakeholders to ensure successful adoption and change management • Build frameworks and tools that enable non-technical teams to leverage AI in their daily work • Create self-service AI capabilities that teams can use without engineering support • Measure and communicate the organizational impact of AI enablement initiatives

🎯 Requirements

• 4-6 years of product management experience, preferably in B2B SaaS or operations-focused roles • Experience building internal tools or enterprise software that drives operational efficiency • Track record of shipping products that delivered measurable business impact • Strong analytical skills with ability to quantify ROI and build business cases • Solid understanding of AI/ML capabilities and limitations • Experience working with data science and ML engineering teams • Familiarity with common AI use cases: classification, prediction, optimization, natural language processing, computer vision • Deep curiosity about how businesses operate and where inefficiencies exist • Comfort working with operational metrics: cost per unit, labor efficiency, margin analysis • Excellent communication skills • Ability to build consensus and drive adoption across functions that may be skeptical of change • Bias toward action and data-driven approach to decision making.

🏖️ Benefits

• Flexible work arrangements