What to Expect

The Data Science and Analytics team is dedicated to data-driven innovation within Tesla’s Residential Energy sector, creating and implementing data-driven products that infrom decision-making. The team’s initiatives span all facets of the Residential Energy Operations division, focusing on prominent projects that include the creation and evaluation of rules-based and machine learning systems for predicting labor needs, planning materials, project timeline forecasting, and optimizing installation efficiencies. They develop new data pipelines and refine existing ones that are crucial for software production applications, operations reporting functions, and stakeholder engagements alongside conducting impact evaluations and KPI analyses for specific programs.

What You’ll Do
Conduct incrementality, lift testing, and attribution modeling to optimize demand generation strategies
Collaborate across various functional and product lines to support all Tesla residential energy offerings, including Powerwall, Solar PV, and Wall Connector
Engage in complex problem-solving initiatives, prioritizing solutions over specific tools, to enhance the effectiveness of residential energy products
Analyze customer behavior to predict order conversion and cancellation rates, providing actionable insights to improve customer retention and satisfaction
Develop and implement descriptive, diagnostic, and predictive models to generate valuable insights for operations and engineering teams
Execute causal inference analyses to evaluate the impact of operations programs and mentor analysts to do the same
Maintain a highly organized workflow while managing multiple priorities and projects efficiently
Demonstrate a strong technical acumen, curiosity, and an innovative mindset to explore new approaches and methodologies in data analysis and modeling
Collaborate with cross-functional teams to align on project goals, share insights, and drive data-driven decision-making
Stay informed about industry trends and advancements in data science and related technologies to continuously enhance analytical capabilities
What You’ll Bring
Minimum of 2-4 years of professional experience as a Machine Learning Engineer in a fast-paced environment
Degree in Computer Science, Engineering, Physics, Statistics, Mathematics, or equivalent experience
Demonstrated proficiency in software development utilizing Python, including core libraries such as NumPy and Pandas
Proven experience in the end-to-end deployment of data pipelines for production software applications
Familiarity with machine learning libraries and statistical frameworks, including TensorFlow, Keras, scikit-learn, SciPy, and PyStan/Bayesian Inference
Proficient in version control systems (Git) and comfortable executing command-line operations bash
Skilled in code testing methodologies using unittest and pytest
Understanding of various data communication protocols REST API, Websockets, and familiarity with continuous integration tools Docker, Jenkins, or Kubernetes
Strong passion for data and its role in driving informed decision-making

Palo Alto, California

Full time