Data Scientist, BMS


Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. 

As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. 

This is Who YOU ARE:

Your main responsibility will be extracting insights from the battery data across fleet of electric cars and build machine learning models to provide valuable estimations and predictions to the product and the user. You will work closely with the battery experts (modeling/electrochemical/mechanical/thermal/software) engineers to define features & metrics from the domain knowledge. You will explore different features and performance metrics of battery and electric vehicle under different usage and aging conditions of the batteries. You will also explore different feature extraction methodologies to discover insights. You will utilize different supervised/unsupervised machine learning models and to build predictive models which includes battery quality metrics, performance metrics of power/energy and remaining useful life


All the while, you will be developing an understanding of the functionality of the BMS and how a high voltage battery function. You must be creative, clever, proactive, driven by detail, and an individual who can work independently, or with other embedded programmers, software architects, and application engineers.



  • Work with vehicle telemetry data at scale to provide insight on the battery health, performance, and powertrain efficiency of Rivian products 
  • Work with battery scientists to improve state of health estimation for batteries based on field data models.
  • Develop statistical and machine learning models for predicting and forecasting field failures, cell chemistry variations, battery health degradation and how various driving behaviors affect battery performance for safety and drivability.
  • Build scalable full stack infrastructure and data pipelines to aggregate telemetry and service data in order to monitor and predict failures in the field. 
  • Research and development of multivariate models for prognostics applications, predictive maintenance, battery state of health estimation, battery remaining useful life.
  • Run cloud-based data models to assist improving battery power and energy estimation based on current, voltage and temperature data points across various drive cycles and geographies.
  • Work with embedded systems engineers to bring cloud-based data models to run on embedded systems.
  • Build a scalable battery fault identification using big data analysis.
  • Develop a machine learning model for aging estimation and lifetime prediction.
  • Develop fleet level fast charging algorithms using statistical and machine learning models.


  • PhD or Masters in Computer Science, Mathematics, Chemistry, Mechanical, Mechatronics, or Chemical Engineering.
  • 3+ years of industry experience in a data science or software engineering role. 

Domain Knowledge: 

Must have Data Science domain knowledge: 

  • Experience in statistical and machine learning concepts 
  • Experience working with data in Apache Spark, Python and SQL
  • Strong fundamentals in software engineering with Python 
  • Data visualization and communication best practices


  • Experience with cloud infrastructure AWS/GCP with terraform/docker 
  • Experience with non-linear state and parameters estimation techniques (Bayesian estimation).
  • Experience with prognostics and health management 
  • Working knowledge of mathematical or advanced machine learning concepts 


This is where you’ll work:

Department:  Battery Management System

Location: Irvine CA or Palo Alto, CA

Equal Opportunity

Rivian is an equal opportunity employer and complies with all applicable federal, state, and local fair employment practices laws. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, ancestry, sex, sexual orientation, gender, gender expression, gender identity, genetic information or characteristics, physical or mental disability, marital/domestic partner status, age, military/veteran status, medical condition, or any other characteristic protected by law.

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