Boom is building Symphony, the first purpose-built jet engine for sustainable supersonic passenger flight, and needs to extract insights from complex datasets to fuel innovation and decision-making.
Requirements
- Strong background in statistics, machine learning and software development; particularly in noisy, empirical datasets requiring wrangling, normalization, and tidying
- Proficient in writing high quality software tooling, particularly in Python and MATLAB. Deep familiarity with industry standard numerical computing toolsets such as scipy, numpy, pandas or Mathworks Statistics and Machine Learning Toolbox
- Background in data classification, clustering and data driven modeling. You should have experience in identifying and utilizing the appropriate machine learning techniques given parameters of the problem and data
- Familiarity with common, conventional high density data storage formats such as: parquet, avro, HDF5, and netCDF; as well as in-transit data formats such as arrow, protobuf, and ethernet based formats
- Experience in systems programming languages, particularly Rust or Go
- Designing systems for anomaly detection
- Prior work with time series analysis, real time data visualization, or databases
Responsibilities
- Collaboratively plan, develop, and deliver customized data analyses that lead to data driven and actionable results
- Develop high quality software tooling that stitches together the data fabric at Boom. This includes planned code requirements, source control, automated tests and deployment workflows
- Support real-time data collection and visualization for test events
- Design, build and deploy ETL pipelines feeding into cloud storage solutions
- Approach problem-spaces at Boom with a hypothesis driven, scientific mindset. Turn qualitative observations into quantitative data points with error bars. Back up results with statistical reasoning. Extract meaning from complex, noisy datasets. Combine heterogenous datasets in useful ways
- Employ tried and true statistical, artificial intelligence, machine learning and numerical methods in relevant use cases to unlock relationships between data and systems. Know when to push the envelope and deploy state-of-the art methods when conventional methods fall short
Other
- History of collaborative projects embedded in technical domains, especially in scientific or engineering spaces. Desire to embed themselves in high-stakes environments where data outputs have influence over mission success or failure
- Demonstrates a strong work ethic and ambition, complemented by effective collaboration skills in a team setting
- Excels in a high-energy, independent, and challenging work environment
- Masters or Doctorate in a Data Science field, or a proven track record and portfolio
- applicant must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State
