Job Description
The role involves designing and deploying advanced machine learning models to optimize Samsara's global supply chain, including demand forecasting, inventory management, and risk modeling, while collaborating cross-functionally and ensuring scalable, operational AI solutions.
Key Responsibilities
- Design, develop, and deploy machine learning and statistical models for supply chain optimization
- Build predictive models for demand forecasting, inventory management, and supply risk assessment
- Create data pipelines and ETL processes using large-scale ERP, IoT, and third-party datasets
- Implement production-grade ML systems supporting batch and real-time inference with MLOps best practices
- Collaborate with cross-functional teams to align data requirements, integration, and change management
- Enhance data infrastructure and analytics platforms for real-time model training, monitoring, and inference
- Mentor junior data scientists through code reviews and collaborative projects
- Lead initiatives to identify and address gaps in data, tools, and processes related to AI and analytics
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, Electrical Engineering, Operations Research, 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, with experience developing and deploying production machine learning 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 such as Tableau and Power BI, and cloud platforms such as AWS, GCP, or Azure.
- Deep statistical expertise, including designing and validating experiments, A/B testing, multi-armed bandits, and applying Bayesian methods for uncertainty quantification.
- Proven experience building advanced forecasting models such as Prophet, LSTMs, or Transformer-based models for intermittent demand and seasonal patterns.
- Skilled in profiling machine learning workloads and reducing cloud GPU spend through model compression, quantization, 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 contributing to or leveraging open-source projects.
Benefits & Perks
Competitive total compensation package including base salary, bonus, and restricted stock unit awards (RSUs)
Employee-led remote and flexible working arrangements
Health benefits
Opportunities for career growth and development
Inclusive work environment with accommodations for persons with disabilities
Support for work in hybrid, remote, or in-person settings
Ready to Apply?
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