Compensation Overview

About the role:

Samsara’s Hardware Reliability and Quality team enables an exceptional customer experience by building reliable hardware, identifying opportunities to improve customer experience, and engaging cross-functionally to resolve key issues. In this role, a successful candidate will create dashboards, queries, data automation tools, and models to provide insight into the health of Samsara’s IoT products, from manufacturing to field performance.

You should apply if:

• You want to impact the industries that run our world: The software, firmware, and hardware you build will result in real-world impact—helping to keep the lights on, get food into grocery stores, and most importantly, ensure workers return home safely.
• You want to build for scale: With over 2.3 million IoT devices deployed to our global customers, you will work on a range of new and mature technologies driving scalable innovation for customers across industries driving the world’s physical operations.
• You are a life-long learner: We have ambitious goals. Every Samsarian has a growth mindset as we work with a wide range of technologies, challenges, and customers that push us to learn on the go.
• You believe customers are more than a number: Samsara engineers enjoy a rare closeness to the end user and you will have the opportunity to participate in customer interviews, collaborate with customer success and product managers, and use metrics to ensure our work is translating into better customer outcomes.
• You are a team player: Working on our Samsara Engineering teams requires a mix of independent effort and collaboration. Motivated by our mission, we’re all racing toward our connected operations vision, and we intend to win—together.

In this role, you will: 

• Create and maintain dashboards, queries, and models that bring visibility to development test results, manufacturing data and field health for Samsara products.
• Perform a variety of exploratory analyses that help us understand the health of our products and identify opportunities for improvement and optimization.
• Partner with hardware, firmware, support, product, and manufacturing teams to understand broad problem statements, and translate those broad problem statements into specific data pipeline/visualization plans.
• Take a practical approach to data analytics, referencing realistic thresholds and cross-referencing with field examples to ensure data is high quality.
• Communicate results and gather feedback to technical and non-technical cross-functional teams.
• Build robust, flexible, and automated software tools to enable complex analysis of real-time fleet.
• Contribute to the automation and standardization of our data pipelines.
• Build visualizations to effectively communicate results.
• 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:

• 5+ years experience as a data scientist or related role.
• High level of proficiency using SQL, Python, and R.
• MS or equivalent coursework in quantitative discipline (e.g., Statistics, Mathematics, Computer Science, Economics, etc.).
• Understanding of reliability analytics and methodologies.
• Answer complex questions on customer usage and behavior to enable proactive monitoring.
• Experience with data manipulation and processing, preferably in SQL or Python (e.g., using PySpark or Pandas).
• Experience building statistical and other analytical models, preferably in Python (e.g., using scikit-learn, NumPy, etc.).
• Experience performing data-based triage of hardware products.
• Understanding of remaining useful life estimates and the various methodologies to generate the estimates (physics-based, machine learning-based, data-based, hybrid models).

An ideal candidate also has:

• Experience in ensuring the integrity of data sources.
• Familiarity with Databricks.
• Familiarity with IoT products and data.
• Solid understanding of statistics (Weibull distribution, Maximum Likelihood Estimation, Bayesian methods, Monte Carlo analysis, etc.).
• General knowledge of physics and engineering principles.
• General Knowledge of data pipelining.
• Experience with data visualization techniques.