Company

TeslaSee more

addressAddressPalo Alto, CA
type Form of workFull-time
CategoryInformation Technology

Job description

In 2023 Elon and Tesla released: The Master Plan Part 3. In this part of the plan, we highlighted the need to deploy 18.3 TW of solar and 46.2 TWh of battery storage to meet global electrification demand. In the United States, the target is 3,052 GW of solar and 6.5 TWh of battery storage, which Tesla is accelerating towards through multiple channels. For Tesla Residential Energy, this means we need to install 150 GW of solar and 1.2 TWh of battery storage at 15 million homes. In order to achieve this goal, we build and deploy innovative energy products like Powerwall, Solar Roof, and Retrofit Solar through a combination of direct labor, sub-contractors, and certified installers.

The Data Science & Analytics team mission is to build and deploy data-driven products that help Tesla's Residential Energy business make smarter decisions. This team supports all branches of the Residential Energy Operations business on a broad range of high-visibility projects including: The development, deployment, and evaluation of both rules-based and machine learning pipelines for labor prediction, proactive material planning, customer project timeline estimation, and installation efficiency optimization. Engineering new data pipelines and optimizing the performance of existing data pipelines depended upon by production software applications (appointment scheduling, design, contracts/pricing), operations reporting teams, and other business stakeholder. Branch-specific program impact evaluation and KPI reporting.

Request

  • Extensiveexperience writing software with Python
  • Experiencewith end-to-end deployments of data pipelines consumed by production softwareapplications
  • Experiencewith multiple data architecture paradigms (SQL, NoSQL, Kafka, Spark)
  • Knowledgeof various data communication protocols (REST API, Websockets)
  • Experiencewith open-source machine learning libraries and frameworks(Tensorflow, Keras, SKLearn, SciPy, PyStan/Bayesian Inference) 
  • Experiencewith version control (Git)
  • Experiencewith command line operations (bash, zsh)
  • Comfortablewith code testing (unittest, pytest)
  • Familiaritywith continuous integration pipelines (Docker, Jenkins, Kubernetes)
Refer code: 8980639. Tesla - The previous day - 2024-04-11 17:02

Tesla

Palo Alto, CA
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