Student Research Assistant researching digital storytelling and patient - centred cancer communication for Okanagan College’s BC Cancer project. Preparing annotated bibliographies and collaborating with patient partners.
About the role
- Staff Applied Scientist building production ML systems for Samsara’s IoT-connected hardware supply chain. Forecasting demand, optimizing inventory, and modeling supplier risk.
Responsibilities
- Define the end-to-end AI transformation roadmap for supply chain alongside the Senior Director of Supply Chain AI Transformation, aligning with company OKRs and executive stakeholders
- Design, train, validate, and deploy production ML models for demand forecasting, inventory optimization, supplier risk scoring, cellular spend prediction, and hardware cash flow
- Build predictive models forecasting demand, inventory, lead times, and spend across the global supply network
- Create features from large-scale ERP, IoT, and third-party datasets and build pipelines and ETL jobs
- Deliver production-grade code supporting batch and real-time inference with MLOps best practices
- Act as AI liaison to Product, Engineering, Procurement, and Finance
- Enhance data infrastructure and analytics platforms for real-time model training, monitoring, and inference at scale
- Mentor junior scientists through code reviews and collaborative project work
- Serve as a scientific voice in roadmap planning, experimentation frameworks, and modeling strategy
- 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 Operations management to drive AI adoption, define processes, and train supply chain teams
- Champion Samsara's cultural principles as the company scales globally
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
- Expertise in statistical modeling and ML, including time series forecasting, optimization, anomaly detection, and causal inference
- Strong Python coding skills and fluency in SQL
- Proven experience developing and deploying production ML systems
- Proficiency in MLOps practices, including automated testing, CI/CD, model versioning, monitoring, and performance tracking
- Experience building real-time inference pipelines and managing GPU/TPU resources for training at scale
- Familiarity with Tableau, Power BI, and cloud platforms including AWS, GCP, and Azure
- Passion for operational excellence, cost efficiency, and scalable solutions
- Track record of taking products or systems from 0 to 1
- Exceptional problem-solving, critical thinking, and communication skills
- Track record of cross-functional collaboration and driving adoption of data-driven solutions
- Ability to design and validate A/B tests and multi-armed bandits and apply Bayesian methods for uncertainty quantification
- Experience building advanced forecasting models such as Prophet, LSTMs, or Transformer-based models
- Experience with model compression, including quantization and pruning, and serverless architectures
- Ability to deploy low-latency inference across multiple geographic regions with fail-over and disaster-recovery strategies
- Experience evaluating and integrating third-party ML platforms and relevant open-source projects
- Deep understanding of supply chain concepts including S&OP, IBP, safety stock, and EOQ
- Experience with ERP systems including SAP, NetSuite, E2DP, and Propel
- Ideal background in consumer electronics or B2B hardware manufacturing
Benefits
- Initial RSU grant with no vesting cliff
- Ongoing refresh equity opportunities tied to performance
- Performance-based bonus/variable pay
- Flexible, employee-led remote model
- Professional development stipend
- Comprehensive health plans
- Parental leave plans
- Above-market total compensation program
- Remote, hybrid, and in-person work options depending on role and operational requirements
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