About the role:

The Samsara AI team builds end-to-end machine learning and computer vision solutions for our customers as well as core ML infrastructure for Samsara. As a Senior Data Scientist you will be a science leader and key contributor in building core models and predictive techniques for use in building and understanding products. You will work closely with other scientists as well as full-stack, firmware, and platform teams to deliver core infrastructure, services, and optimizations.

This role can be office-based or fully remote in the US and Canada.

In this role, you will: 

• Build and improve the accuracy of ML / CV models, including retraining and optimizing open-source models to solve Samsara-specific problems 
• Work with petabyte-scale data from Samsara camera and sensor devices to develop new models
• Lead org-wide initiatives on science, testing, and data-driven decision making
• Research, adapt, and apply cutting edge techniques in CV and deep learning
• Optimize models for inference on the backend and/or on edge devices
• Partner with our hardware teams to design devices for optimal performance and cost
• Stay connected to industry and academic research and adopt novel technology that suits Samsara’s needs.
• 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

Minimum requirements for the role:

• BS or MS in Computer Science or other quantitative field (e.g., Applied Math, Statistics)
• 8+ years experience as a Data Scientist, Machine Learning Engineer, or similar role
• Proficiency in data modeling as well as SQL, Python, R, or a similar scripting language
• Ability to distill informal or ambiguous customer and business requirements into crisp problem definitions
• Proven ability to communicate verbally and in writing to technical peers and leadership teams with various levels of technical knowledge
• Experience coaching and mentoring more junior scientists

An ideal candidate also has:

• PhD in Computer Science or quantitative discipline (e.g., Applied Math, Physics, Statistics)
• Strong functional knowledge of common tools like Jupyter Notebooks, AWS (e.g., EMR, Redshift, S3, Sagemaker)
• Record of successful mentoring and development of junior team members
• Experience working with large datasets using distributed computing (e.g., Spark, Hive)