Joby Barnard
Overview
Joby 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. Since 2009, our team has worked steadily to make this dream a reality. We’ve designed and tested many generations of prototype aircraft capable of serving in a network of electric air taxis. We’re looking for talented, committed individuals to join our team as we push onward toward certifying the Joby aircraft, scaling our manufacturing, and launching our initial commercial service.
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
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: 6+ years of experience writing complex, performant SQL queries and designing efficient data models and pipelines for analytical purposes
Advanced proficiency with Spark: 4+ years distributed computing frameworks preferably in a cloud environment 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 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
Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $151,900- $202,500 per year salary. 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.
Additional Information Joby is an Equal Opportunity Employer.
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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
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: 6+ years of experience writing complex, performant SQL queries and designing efficient data models and pipelines for analytical purposes
Advanced proficiency with Spark: 4+ years distributed computing frameworks preferably in a cloud environment 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 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
Compensation at Joby is a combination of base pay and Restricted Stock Units (RSUs). The target base pay for this position is $151,900- $202,500 per year salary. 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.
Additional Information Joby is an Equal Opportunity Employer.
#J-18808-Ljbffr