Staff Data Scientist
- 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
- 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
- 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
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