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Staff Data Scientist – Prognostics and Health Monitoring

3 months ago

Santa Cruz, United States Joby Aviation Full time
Overview

Joby Aviation is seeking a skilled Data Scientist with a specialized background in developing Prognostics and Health Monitoring (PHM) algorithms, specifically for aircraft or heavy machinery applications. The ideal candidate will bring a proven track record in contributing to the research and development of cutting-edge diagnostic and prognostic solutions for critical applications. As a valuable member of our dynamic team, the successful candidate will play a pivotal role in designing innovative diagnostics and prognostics algorithms tailored for the Joby S4 vehicle. This position involves active participation in all stages of the product development lifecycle, requiring expertise in requirements gathering, design, validation, documentation, release, and maintenance. You will also need to work closely with your colleagues across a broad set of highly technical disciplines who depend on data. The ideal candidate is energetic, has a positive attitude, is flexible and excited about learning and using new technologies.  

Responsibilities
  • Develop models and scalable algorithms derived from both data and underlying physics to evaluate the condition of electro-mechanical systems
  • Create predictive models of physical degradation, failures, and data-driven prognostics algorithms to assess the health and performance of critical components
  • Formulate health monitoring strategies to detect anomalies/outliers in real flight data
  • Conduct research and development projects concentrating on Prognostic Health Management (PHM) and Condition Based Maintenance Plus (CBM+) in diverse application areas
  • Gather and analyze equipment lifecycle history, including failure modes, downtime, MTBF, etc.
  • Address complex questions regarding fleet usage and behavior to facilitate proactive monitoring, enhance reliability, and minimize field failures
  • Wrangle data from a multitude of formats and systems (Avro, TDMS, PostgreSQL, AWS, etc.)
  • Conduct data analysis and interpret sensor data from a number of physical tests (aircraft, simulators, reliability test equipment, subsystem tests, etc.)
  • Collaborate with cross-functional teams and subject matter experts to integrate prognostic solutions into existing systems and workflows
  • Support field testing and validation efforts, working closely with test engineers and technicians to validate prognostic models in real-world environments
  • Automate data collection and processing algorithms, including the generation of Machine Learning/Deep Learning models for automatic data labeling and anomaly detection
  • Comfortable navigating a quickly changing environment and willing to learn on-the-fly to obtain and define requirements
  • Stay current with advancements in machine learning, particularly in the areas of data-driven prognostics and physics-based machine learning

The primary location for this role is in Santa Cruz CA. but an alternate location of San Carlos, CA. is also possible.

Required
  • MS or PhD in Electrical, Computer Science, Mechanical, or Aerospace Engineering, or similar field and 7+ years of relevant experience.
  • Work experience, including academic research, directly related to the development of Prognostics Health Management (PHM) or Condition Based Maintenance Plus (CBM+) technologies, or analysis and simulation of complex electrical or mechanical systems 
  • Strong background in data analysis (algorithms, data structures, and architectures), probability, statistics, signal processing, and predictive modeling 
  • Experience with rotating machinery diagnostics and vibrational analysis, preferably in the aerospace industry 
  • Strong programming skills, preferably in Python and its numerical and data libraries (pandas, scipy, numpy, etc.) 
  • Work experience with Apache Spark or other big data tools (Databricks, Presto, Data Lake, etc.) 
  • Work experience with anomaly/outlier detection in time series data 
  • Proficient in data-driven methods, Regression, Neural Networks, Machine Learning and Deep Learning 
  • Strong written and verbal communication skills 
  • Self-starter capable of working with limited supervision and guidance 
  • Experience leading teams and projects from conception to completion 
  • Strong verbal and written communication skills 
Desired
  • Experience with Health and Usage Monitoring Systems (HUMS) and their certification
  • Knowledge of aerospace systems, components, and their failure modes
  • Familiarity with fault detection and diagnosis methods, and reliability analysis
  • Experience with MLOps and building machine learning pipelines in a professional setting
  • Record of generating new ideas or improving existing ideas in statistical modeling or machine learning, demonstrated by accomplishments such as first-author publications or projects
  • Experience with data architectures in relation to how to store, fetch, and manipulate data (SQL, custom APIs, etc.)
  • Used Git in small to medium size teams for code reviews.

 

Please still apply if you don’t meet all items in the desired section Studies have shown that women and people of color are less likely to