Product Manager, Data Science Platform (AI/ML)

 

Why project44?  

At project44, we believe in better.  

We challenge the status quo because we know a better supply chain isn’t just possible—it’s essential. Better for our customers. Better for their business. Better for the world.  With our Decision Intelligence Platform, Movement, we’re redefining how global supply chains operate. By transforming fragmented logistics data into real-time, AI-powered insights, we empower companies to connect instantly, see clearly, act decisively, and automate intelligently. Our Supply Chain AI enhances visibility, drives smarter execution, and unlocks next-gen applications that keep businesses moving forward.  Headquartered in Chicago, IL with a 2nd HQ in Bengaluru, India we are powered by a diverse global team that is tackling the toughest logistics challenges with innovation, urgency, and purpose.  If you’re driven to solve meaningful problems, leverage AI to scale rapidly, drive impact daily, and be part of a high-performance team – we should talk. 

We’re hiring a technical, hands-on Product Manager to scale ML-powered products (ETA, risk, anomalies/exceptions, network insights) while building Data Science as a Platform—reusable capabilities that accelerate model development, deployment, monitoring, and governance across multiple product areas. 

You’ll partner daily with Data Science, ML Engineering, Data Engineering, Platform Engineering, and Product teams to ship production ML and establish repeatable, measurable ML delivery. 

Key Accountabilities

• Own the roadmap for applied ML capabilities beyond ETA (risk scoring, exception prediction, anomaly detection, carrier/network performance insights), from discovery to launch and iteration. 
• Define and deliver a DS/ML platform: feature management, experimentation, model registry, deployment patterns, monitoring/observability, governance, and self-serve tooling. 
• Translate customer workflows into ML problem statements: labels/targets, constraints, SLAs, interpretability, and “do no harm” launch gates. 
• Drive evaluation and experimentation: offline metrics/back testing, online testing (A/B, holdouts), and measurable business impact. 
• Partner with engineering on batch + real-time inference architectures, streaming/event-time feature needs, and reliability (SLOs, incident playbooks). 
• Establish and track platform success metrics: time-to-first-model, deployment frequency, reuse rate, model performance stability, incident rate, and ROI.

Minimum Qualifications

• 5–8+ years Product Management experience with 3–4+ years focused on DS/ML-driven products (or equivalent)
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