Senior Applied Scientist II

Summary

Join Samsara's Supply Chain Innovation & Intelligence team as a Senior Applied Scientist II and design, develop, and deploy advanced machine learning and statistical models to optimize the global supply chain. You will forecast demand, model supply risk, and collaborate with cross-functional teams. This role requires end-to-end ownership, from ideation to deployment, and involves working with large datasets to solve complex problems. You will mentor junior scientists and contribute to the team's roadmap and scientific agenda. The position is open to US-based candidates outside of the San Francisco Bay Area, NYC Metro Area, and Washington, D.C. Metro Area. This role offers the opportunity to make a significant impact on Samsara's operations and contribute to a growing, innovative company.

Requirements

• 8+ years of experience in applied data science or machine learning, ideally in supply chain, operations research, logistics, or manufacturing
• Master’s or PhD in Computer Science, Statistics, Data Science, EE, OR, or a related technical field
• Expertise in statistical modeling or machine learning, including time series forecasting, optimization, and anomaly detection
• Strong coding skills in Python and fluency in SQL; experience developing and deploying production ML systems
• Familiarity with MLOps practices, including automated testing, CI/CD, model versioning, and monitoring
• Demonstrated track record building real-time inference pipelines and managing GPU/TPU resources
• Familiarity with data visualization tools (e.g., Tableau, Power BI) and cloud platforms (e.g., AWS, GCP, or Azure)
• A passion for operational excellence, cost efficiency, and building scalable, data-driven solutions
• Exceptional problem-solving, critical thinking, and communication abilities

Responsibilities

• Define the end-to-end AI transformation roadmap for supply chain alongside the Director, aligning with company OKRs and working closely with executive stakeholders
• Own the design, training, validation, and deployment of ML and statistical models
• (e.g., demand forecasting, inventory optimization, supplier risk scoring), ensuring robust MLOps practices and measurable ROI
• Build predictive models to forecast demand, lead times, and cellular spend across Samsara’s global supply network
• Create novel features using large-scale ERP, IoT, and third-party datasets; build pipelines and ETL jobs to serve models and stakeholders
• 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—ensuring alignment on data requirements, integration, and change management
• Drive enhancements to our data infrastructure and analytics platform to support real-time model training, monitoring, and inference at scale
• Mentor junior scientists through code reviews and collaborative project work
• Act as a key scientific voice in roadmap planning, experimentation frameworks, and modeling strategy discussions
• Identify gaps in data, tools, and processes—and lead initiatives to close them
• Establish governance frameworks, documentation standards, and quality controls for model development, validation, and lifecycle management
• Partner with Ops management to drive adoption of AI tools, define new processes, and train supply chain teams on insights-driven workflows
• Hire, develop and lead an inclusive, engaged, and high-performing team
• Champion, role model, and embed Samsara’s cultural principles (Focus on Customer Success, Build for the Long Term, Adopt a Growth Mindset, Be Inclusive, Win as a Team) as we scale globally and across new offices

Preferred Qualifications

• 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
• Cross-Region Scaling: Demonstrated ability to deploy low-latency inference across multiple geographic regions with fail-over and disaster-recovery strategies
• Vendor & Open-Source Savvy: Experience evaluating and integrating third-party ML platforms and contributing to or leveraging relevant open-source projects

Benefits

Full time employees receive a competitive total compensation package along with employee-led remote and flexible working, health benefits, Samsara for Good charity fund, and much, much more