iCIMS_JobContent"> Senior Data Scientist - Prognostic and Health Monitoring (HUMS) Job Locations US-CA-Santa Cruz ID 2026-5238 Category Product Type Regular Full-Time Company Overview Imagine a piloted air taxi that takes off vertically, then quietly carries you and your fellow passengers over the congested city streets below, enabling you to spend more time with the people and places that matter most. At Joby, we've been working to make that dream a reality since 2009 and we're now in the final stages of certifying our aircraft with the FAA. With plans to launch our aircraft in the US and Dubai, we're now scaling manufacturing and preparing for the launch of our commercial service. Overview 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. Responsibilities &bull; Develop Health Algorithms: 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) &bull; Partner with Subject Matter Experts (SMEs): 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 &bull; Characterize Physical Behavior & Operational Loads: 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 &bull; Component Usage Tracking & Damage Modeling: 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 &bull; Write Production-Grade Code: 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 &bull; Own Pipeline Architecture: 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 &bull; Support Flight & Field Validation: Work alongside test engineers and technicians to validate and harden health-monitoring solutions using real-world physical tests &bull; Drive Tooling Innovation: 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 Required &bull; MS or PhD in Aerospace, Mechanical, Electrical Engineering, Computer Science, or a related technical field &bull; 3+ years of post-graduate experience (or equivalent) focused on PHM, Condition-Based Maintenance (CBM+), or the analysis of complex electro-mechanical systems &bull; 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 &bull; Self-driven, intellectually curious, and eager to learn and adopt new technologies &bull; Demonstrated ability to independently own implementation architecture and project lifecycles from ingestion to deployment with minimal supervision &bull; Demonstrable foundations in signal processing, time-series analysis, and frequency-domain fundamentals necessary to interpret physical sensor data &bull; Strong background in data analysis (algorithms, data structures, and architectures), probability, statistics, signal processing and predictive modeling &bull; Proven experience applying regression, neural networks, and machine/deep learning specifically for anomaly detection and fault isolation in physical hardware &bull; 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 &bull; Strong collaborative and communication skills, with a track record of effectively working alongside multidisciplinary engineering teams Desired &bull; Deep understanding of rotating machinery diagnostics, vibration analysis, and aerospace failure modes. Familiarity with HUMS/AHM/IVHM certification processes is a massive plus &bull; Hands-on experience applying Large Language Models (LLMs), agentic frameworks, Retrieval-Augmented Generation (RAG), or advanced prompt engineering to accelerate technical workflows, automate data labeling, or build internal engineering assistance tools &bull; Experience building, monitoring, and maintaining ML pipelines in a high-stakes, safety-critical professional production environment &bull; Strong familiarity with relational databases (SQL, PostgreSQL) and designing custom APIs to seamlessly fetch and manipulate distributed data Additional Information Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $147,200 - $179,800/yr. The compensation package will be determined by job-related knowledge, skills, and experience. Joby also offers a comprehensive benefits package, including paid time off, healthcare benefits, a 401(k) plan with a company match, an employee stock purchase plan (ESPP), short-term and long-term disability coverage, life insurance, and more. Joby is an Equal Opportunity Employer <div class="