About the role:

Samsara’s Hardware 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. 

In this role, you will: 

• Create and maintain dashboards, queries, and models that bring visibility to 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:

• 7+ years experience as a data analyst or related role
• High level of proficiency using SQL, Python, and Tableau
• BS or equivalent coursework in quantitative discipline (e.g., Statistics, Mathematics, Computer Science, Economics, etc.)
• Expert understanding of reliability analytics and methodologies
• Apply statistical analysis to test (accelerated life) and field (life) data to inform reliability modeling/analyses and associated corrective actions
• 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.)
• Work closely with Reliability and Design engineers to create/interpret/validate numeric models of fielded and in-test products

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