Data Scientist 2

ULA•Denver, CO

About The Position

The Data Scientist 2 provides enterprise-level data analytics and data science support through the Systems Engineering organization. The role interprets data and analyzes results using statistical techniques, develops and implements models using diverse datasets, and creates visualizations and reports to communicate findings to technical and non-technical stakeholders. The position works with people across the enterprise to develop and continuously improve tools and methods for analyses, and participates in multidisciplinary teams addressing customer and operational needs across engineering, manufacturing, launch operations, and business functions. The successful candidate selects the appropriate scale of data science activities for each project and applies analytics to support decision-making. The role calls for a self-starter who can work autonomously and collaboratively, communicate effectively, and maintain focus and consensus within diverse teams. Aerospace analytics experience is desired; knowledge supporting launch-vehicle or satellite-vehicle design, analysis, production, or operations may also be relevant.

Requirements

Bachelor’s degree in Engineering, Data Science or Analytics, Statistics, Applied Mathematics, or a related field.
At least 2 years of related work experience, with emphasis in Data Analytics/Data Science.
Technical expertise in data models, data mining, data extraction, and data wrangling techniques.
Strong knowledge of and experience using statistical or analytical packages for small and big data, such as Python, R, and/or MATLAB, and using them to derive business insights.
Experience applying machine learning and/or artificial intelligence to business or engineering applications.
Strong analytical skills, including collecting, organizing, analyzing, and disseminating significant amounts of information with attention to detail and accuracy.
Ability to query data, write reports, and present findings.
Ability to work independently and as part of a team.
Excellent written and oral communication skills and organizational skills to maintain focus and achieve consensus in multidisciplinary teams.
Ability to work autonomously, learn and execute processes, and collaborate effectively.
Applicants must be U.S. Persons as defined by ITAR because the position requires use of ITAR-controlled information.

Nice To Haves

Master’s degree in Engineering, Data Science or Analytics, Statistics, Applied Mathematics, or a related field.
Experience in the aerospace industry with existing and evolving launch systems or satellite systems.
Knowledge of both engineering and statistics/data science.
Experience with cloud-based analysis or modeling, such as AWS, Azure, or GCS.
Basic understanding of launch vehicles.
Self-motivation, initiative, teamwork, and leadership skills demonstrated on technical projects.
Demonstrated experience in data analytics in an aerospace environment.
Technical knowledge supporting launch-vehicle and/or satellite-vehicle design, analysis, production, or operations.
Experience working on multidisciplinary teams to resolve technical and programmatic issues using current or advanced statistical and data science techniques.

Responsibilities

Interpret data and analyze results using statistical techniques; prepare reports, presentations, and technical documents.
Design, analyze, build, test, evaluate, and implement models using diverse datasets to solve company problems.
Develop visualizations of datasets and modeling results.
Communicate results clearly to engineers, leadership, and technical and non-technical stakeholders.
Work with people across the enterprise to develop appropriate analysis tools and methods.
Continuously improve the tools and processes used to perform data science activities.
Participate in multidisciplinary project teams to evaluate customer and operational needs and develop solutions that support decision-making across engineering, manufacturing, launch operations, and business disciplines.
Identify and apply an appropriate scale for data science activities based on project needs.