Senior Data Scientist – Prognostic and Health Monitoring (HUMS)

Joby Aviation•Santa Cruz, CA

4d•$147,200 - $179,800

About The Position

Joby Aviation is seeking a Senior Data Scientist to join our Health and Usage Monitoring Systems (HUMS) team. In this role, you will be driving algorithmic development behind the predictive health, safety, and reliability of our aircraft. You will partner closely with multidisciplinary Subject Matter Experts (SMEs)—across Propulsion, Flight Test, Battery Systems, and Structures—to design, develop, and deploy advanced algorithms that monitor the health and usage of critical Joby subsystems. This is a senior individual-contributor role for an engineer who thrives at the intersection of physical systems and modern data science. You will own your projects end-to-end: translating complex physical degradation phenomena into robust predictive models, and turning those models into production-quality, well-tested code. If you are passionate about blending signal processing, machine learning, and data-engineering to shape the future of electric aviation, we want to talk to you. What we bring to the table is a truly unique data landscape. You will not analyze flight data in a vacuum. Instead, you will integrate high-frequency flight sensor telemetry with comprehensive ground test data, component serial numbers, manufacturing database to construct a unified, definitive source of truth for aircraft health and component tracking. To solve these complex challenges, we foster an innovative environment where you are actively encouraged to leverage the latest technologies and state-of-the-art AI frameworks to accelerate your work.

Requirements

MS or PhD in Aerospace, Mechanical, Electrical Engineering, Computer Science, or a related technical field
3+ years of post-graduate experience (or equivalent) focused on PHM, Condition-Based Maintenance (CBM+), or the analysis of complex electro-mechanical systems
Exceptional, production-quality Python skills (pandas, scipy, numpy, pyspark) with a strict focus on automated testing, CI/CD pipelines, and disciplined version control (Git)—not just Jupyter notebook prototyping
Self-driven, intellectually curious, and eager to learn and adopt new technologies
Demonstrated ability to independently own implementation architecture and project lifecycles from ingestion to deployment with minimal supervision
Demonstrable foundations in signal processing, time-series analysis, and frequency-domain fundamentals necessary to interpret physical sensor data
Strong background in data analysis (algorithms, data structures, and architectures), probability, statistics, signal processing and predictive modeling
Proven experience applying regression, neural networks, and machine/deep learning specifically for anomaly detection and fault isolation in physical hardware
Experience leveraging Apache Spark or similar big data tools to wrangle, process, and analyze massive flight and test datasets. Experience with Databricks is a strong plus
Strong collaborative and communication skills, with a track record of effectively working alongside multidisciplinary engineering teams

Nice To Haves

Deep understanding of rotating machinery diagnostics, vibration analysis, and aerospace failure modes. Familiarity with HUMS/AHM/IVHM certification processes is a massive plus
Hands-on experience applying Large Language Models (LLMs), agentic frameworks, or advanced prompt engineering to accelerate technical workflows, automate data labeling, or build internal engineering assistance tools
Experience building, monitoring, and maintaining ML pipelines in a high-stakes, safety-critical professional production environment
Strong familiarity with relational databases (SQL, PostgreSQL) and designing custom APIs to seamlessly fetch and manipulate distributed data

Responsibilities

Design, build, and validate data-driven and physics-informed models to evaluate the condition, degradation, and Remaining Useful Life (RUL) of critical Joby subsystems (e.g., propulsion, batteries, actuation, and structures)
Collaborate closely with domain experts across Flight Physics, Aircraft Design, Flight Test, Reliability, and Systems Engineering to translate physical failure modes and structural loads into actionable diagnostics and prognostic algorithms
Deeply analyze aircraft physical behavior and actual operational loads by wrangling complex sensor and time-series data from flights, simulators, and subsystem test rigs. Use these insights to isolate anomalies, detect early faults, and map the long-term degradation of critical components
Develop algorithmic frameworks to track component-level operating metrics, flight cycles, and life limits. Translate real-world operational loads into cumulative fatigue/damage models to monitor and inform fleet-wide asset component replacement
Turn prototypes into clean, well-tested, maintainable, and production-ready Python code. Participate in and actively raise the bar for team code reviews and engineering best practices
Design, build, and own robust, end-to-end data pipelines and services that scale efficiently to process massive volumes of raw flight and test data
Work alongside test engineers and technicians to validate and harden health-monitoring solutions using real-world physical tests
Selectively evaluate and integrate advanced ML/AI methodologies (such as automated data labeling or diagnostic assistance tooling) where they genuinely accelerate Prognostics Health Monitoring (PHM) workflows and team efficiency