Staff Data Scientist

Joby Aviation • Santa Cruz, CA

14h

About The Position

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.

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

Nice To Haves

• 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 processing techniques
• Experience with cloud platforms (AWS, GCP, Azure) and Infrastructure as Code (IaC) tools like Terraform or Kubernetes
• Experience in the aerospace, automotive, battery technology, or another hardware-intensive industry

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

Benefits

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior