Sr. Manager, Reliability Data Science and Analytics

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Apache Hadoop, Scala Programming, Machine learning techniques, Data science techniques, PyTorch, TensorFlow, MapReduce, R Programming

Job Description

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.

Reliability is at the core of Electric Adventure Vehicles. As the Sr. Manager of Reliability Data Science and Analytics, you will play a key role in using data for to predict and improve reliability performance of Rivian products.

The reliability data science team takes on a variety of challenging problems from deriving insights from usage of the existing fielded fleet to inform future design decisions to developing at scale predictive models that inform vehicle maintenance and servicing strategies for our fleet. Reliability Data Science is a highly cross-functional team that partners with teams in reliability, product development, quality, service and manufacturing to drive product improvements.

This is what you’ll do:

• Lead and develop a talented and highly technical team of data scientists and analysts.
• Be a strong technical leader and coach for the team.
• Lead the vision, strategy and roadmap of reliability data analytics and reporting that support continuous product improvements.
• Work with vehicle telemetry data at scale to provide insights on the reliability of Rivian products.
• Lead the development of field performance KPIs and architect the team’s approach to data visualization and dashboarding.
• Develop statistical packages to automate reliability analytics and data visualizations.
• Lead the utilization of parametric data coming from the vehicle for physics-of-failure based reliability models.
• Develop statistical and machine learning models for predicting and forecasting field failures.
• Extract useful statistics and usage profiles from the existing fielded fleet in order to drive accurate design requirements for next generation vehicles.
• Lead the predictive maintenance reliability analytics for our fleets, products and accessories.
• Work cross-functionally with the cloud data infrastructure, IT and other data stakeholders to drive implementation of meaningful data interfaces to support data science for reliability.
• Develop strong partnerships with design, service, manufacturing, testing and other engineering teams across the organization to promote a data driven mindset in decision making around product development, design improvements and servicing strategies. 

This is what you’ll need:

• Minimum requirement is a Bachelors degree in Computer Science, Statistics or an Engineering discipline with 10+ years of industry experience in a data science role.
• Exceptional management skills, attracting and retaining great talent. Preferably five or more years of experience managing highly technical teams of data scientists and analysts.
• Strong fundamentals in software engineering with Python, expert in all major Python libraries for data analysis.
• Working knowledge of statistical and machine learning concepts, experience with developing predictive models.
• Excellent data visualization skills.
• Experience working with data in Apache Spark, Python and SQL.
• Great collaborator and communicator with partners across engineering teams.
• Effective in planning projects, setting deliverables and defining timelines.
• A desire to contribute to a respectful, fun, inclusive and inspiring team culture.

Bonus:

• Masters or PhD in Computer Science, Statistics or Engineering is desired
• Automotive industry experience
• Experience with prognostics and health management
• Experience in physics of failure reliability modeling and survival models
• Experience with data at scale technologies such as hadoop/hive/presto/beam
• Cloud infrastructure AWS/GCP with terraform/docker
• Programming in multiple languages such as golang/scala/C/C++