Analytics Engineer, Thermal & Chassis

Thermal & Chassis Engineering is the team behind the Tesla Heat Pump, Supermanifold, Octovalve, Superbottle, Airwave, Bioweapon Defense Mode and more. Our multi-disciplinary team of mechanical, manufacturing, electrical, aerodynamics, thermal, and analytics engineers works together across all phases of development from idea inception, through prototyping, production, and fleet support.

You’ll work alongside experts with many years of experience, high aptitude recent graduates, and people from every stage in between. Collaboration, learning, and growth are core to the team’s success.

We’re looking for a key player who can help drive data analytics and work cross-functionally to uncover opportunities in product improvements. The candidate should have experience working with large datasets, and using data to help design critical metrics, characterize engineering systems, optimize product performance, and build useful data pipelines, visualizations, and dashboards.

What You’ll Do

Analyze vehicle data and extract useful statistics/insights that drive actionable recommendations to improve product performance/quality and customer experience
Write efficient SQL/Python code and complex queries across extensive data sets
Build effective models to fuel analytical frameworks that drive material product improvements in hardware optimization, controls, and vehicle diagnostics
Work effectively with engineers and conduct end-to-end analyses, from data requirement gathering, to data processing and modeling
Identify, analyze, and interpret trends or patterns in complex data sets and depict the story via dashboards and reports
Automate analyses and author pipelines using SQL, Python, and Airflow based ETL framework

What You’ll Bring

Basic Qualifications:

BS or MS in Mechanical Engineering, Computer Science, Math, Statistics or related field
Prior internship/work experience in data analytics or related field
Proven proficiency in SQL and Python for writing efficient queries, data manipulation, and analysis
Working knowledge of data visualization techniques and tools using Matplotlib, Seaborn, etc.
Experience working with time-series sensors data to perform robust engineering analyses
Good organizational skills and an ability to prioritize and manage multiple projects simultaneously to meet deadlines
Demonstrated ability to take on projects with a sense of ownership and entrepreneurial mindset

Preferred Qualifications:

Experience with distributed computing (Spark /Hive/Hadoop)
First principles understanding of mechanical engineering concepts with relevant project/research experience
Experience creating data products and dashboards in Tableau, R Shiny, or D3
Experience building machine learning models with demonstrated impact
Experience communicating results with stakeholders/leadership, and working cross-functionally across different teams

Expected Compensation: $80,000 - $300,000/annual salary + cash and stock awards + benefits

‌