Staff Data Scientist

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 As a Staff Data Scientist on the Data Analytics core team, you will be a technical leader responsible for deriving critical insights that directly influence the safety, reliability, and performance of our aircraft. This role goes beyond analysis; you will architect and build scalable, production-grade data science solutions, translating complex data from flight tests, manufacturing, and operations into actionable intelligence. You will serve as a mentor and a key technical voice, working across highly technical disciplines to define data strategy and solve our most challenging problems. The ideal candidate is a proactive and seasoned expert who thrives on ambiguity, is passionate about building robust systems, and is excited to apply their skills to the future of transportation.  Responsibilities Responsibilities   Collaborate with data scientists, other cross-functional teams and subject matter experts on software engineering projects  Conduct data analysis and interpret sensor data from a number of physical processes (aircraft, simulators, reliability test equipment, subsystem tests, etc.)   Understand both data systems and physical systems, analyzing high-frequency time-series data from flight tests, battery systems, acoustic sensors, and manufacturing processes to identify patterns, anomalies, and performance trends  Leverage advanced statistical methods, signal processing, and machine learning to fuse disparate data sources and build comprehensive models of complex physical systems  Architect, design, and lead the development of scalable, end-to-end data science and machine learning systems for production use  Define the technical roadmap for data analysis and predictive modeling within key areas of the business, identifying new opportunities to leverage data for strategic advantage  Establish and champion best practices for software engineering, MLOps, and data modeling within the data science team  Mentor and guide junior and senior data scientists, elevating the technical capabilities of the entire team through code reviews, design discussions, and knowledge sharing  Act as a key technical liaison between the data team and other engineering departments (e.g., Aerodynamics, Powertrain, Manufacturing), translating business needs into technical requirements  Develop robust, maintainable, and well-tested Python libraries and tools to automate data processing and analysis pipelines  Design and build insightful dashboards and visualizations to communicate findings clearly to both technical and non-technical stakeholders  Present complex analytical results and strategic recommendations to engineering teams and executive leadership, driving data-informed decision-making  Comfortable navigating a quickly changing environment and willing to learn on-the-fly to obtain and define requirements  Stay current with advancements in software and data engineering  Required Requirements   M.S. or Ph.D. in Computer Science, Engineering, Statistics, or a related quantitative field, or equivalent experience  10+ years of professional, hands on coding experience in data science and machine learning or a related role, with a demonstrated track record of leading complex projects from ideation to production deployment  Expert-level software-engineering: deep expertise in architecting and writing clean, scalable, and maintainable code. You are a thought leader in software design patterns and best practices  Expert proficiency in Python and its core data science libraries (e.g., Pandas, NumPy, Scikit-learn)  Advanced SQL and data modeling experience writing complex, performant SQL queries and designing efficient data models and pipelines for analytical purposes Advanced proficiency in Spark and distributed computing frameworks, with experience in cloud environments like Databricks Strong background in data science, data analysis and visualization (algorithms, data structures, and architectures), probability, statistics, and predictive modeling  Strong background in Machine Learning using packages such as PyTorch, Keras or TensorFlow  Ability to troubleshoot complex issues across multiple levels of abstraction  Proficiency with Unix-based platforms, shell scripting, and Git source control  Experience with data pipeline architectures, ingestion, ETL, transformations, analytics, API connectors and visualization  Strong experience with development and Ops for GenAI LLMs and Machine Learning, with a past record of successful projects delivery end-to-end   Expert use of IDE’s for authoring, refactoring and debugging code  Ability to navigate a quickly changing environment, independently tackle ambiguous problems, and deliver high-impact solutions with limited supervision  Experience leading projects from conception to completion  Proven ability to communicate complex technical concepts to diverse audiences, from junior engineers to executive leadership  Desired Direct experience with anomaly/outlier detection in high-frequency time-series sensor data  Experience developing and deploying models in a production environment using modern MLOps principles and tools (e.g., MLflow, Kubeflow)  Experience with version control and CI/CD platforms, able to manage your software through its entire lifecycle (development, testing, deployment)  Familiarity with physics-based modeling, digital twins, or advanced signal pro