ML/AI - Supply Chain

Samsara is building an AI/ML team inside its Operations team to make its supply chain AI native. You would be one of its first AI/ML engineers. You will design, develop, and deploy advanced machine learning and statistical models that optimize the global supply chain.

Responsibilities:

• Define the end-to-end AI transformation roadmap for supply chain alongside the Senior Director of Supply Chain AI Transformation
• Design, train, validate, and deploy ML models to production (demand forecasting, inventory optimization, supplier risk scoring, cellular spend prediction, HW cash flow)
• Build predictive models to forecast demand, inventory, lead times, and spend across Samsara's global supply network
• Create novel features using large-scale ERP, IoT, and third-party datasets
• Deliver production-grade code that supports both batch and real-time inference with MLOps best practices
• Act as the AI liaison to Product, Engineering, Procurement, and Finance
• Drive enhancements to the data infrastructure and analytics platform
• Mentor junior scientists through code reviews and collaborative project work
• Identify gaps in data, tools, and processes,and lead initiatives to close them

Requirements:

• 8+ years in applied data science or ML, ideally in supply chain, operations research, logistics, or manufacturing
• Master's or PhD in Computer Science, Statistics, Data Science, EE, OR, or related technical field
• Expert in statistical modeling and ML: time series forecasting, optimization, anomaly detection, causal inference
• Strong Python coding skills and fluency in SQL
• Proficiency in MLOps practices: automated testing, CI/CD, model versioning, monitoring, performance tracking

Nice to have:

• Deep Statistical Expertise: Able to design and validate experiments (A/B, multi-armed bandits) and apply Bayesian methods for uncertainty quantification
• Time-Series Mastery: Proven track record building advanced forecasting models (e.g., Prophet, LSTMs, Transformer-based) for intermittent demand and seasonal patterns
• Cost Optimization Mindset: Skilled in profiling ML workloads and driving down cloud/GPU spend through model compression (quantization, pruning) and serverless architectures