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# Data Scientist Asc - E1

- **Company:** Lockheed Martin
- **Location:** Marietta, GA, United States
- **Salary:** $67,000.0 - $124,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Cloud Computing, Software Quality, Code Review, Distributed Systems, Python (Programming Language), Machine Learning, Natural Language Processing, SAS (Software), Software Engineering, Text Mining, Deep Learning, Numerical Computing, Git, Information Technology
- **Published:** September 4, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3377270108&tx=TT5148THD&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Bachelors degree in Engineering, Physics, Computer Science, Software, Data Science, Data Analysis, Physics, Mathematics or equivalent experience/combined education.
 * Knowledge of AI/ML fundamentals
 * Understanding of software development practices - style enforcement, standard testing, software quality, code reviews, etc.

Desired Skills

 * Knowledge of Defence Air System Supply and Maintenance networks and systems
 * SAS, Python, Git Experience
 * Degree in Data Science
 * Strong communication and organizational skills

## Description

Perform exploratory research and analysis to identify novel and meaningful patterns in data and uses statistical methods to reject or accept proposed hypotheses about relationships or latent predictive factors discovered through their work to provide business value. Uses a combination of tools, technologies, and numerical computing systems (i.e.: GPU processing, distributed computing, highly parallel coding, cloud computing, machine learning, visualization, system modelling and simulation) to achieve results. Areas of expertise should include several of the following: applied statistics, text mining, natural language processing, deep learning, optimization, and other similar fields. Work on datasets with applied statistics and machine learning algorithms; Use exploratory data analysis techniques to identify meaningful relationships, patterns, or trends from complex data sets and discover opportunities in datasets to support decision-making; Develop predictive models and new algorithms to
solve data / business problems; Understand the math and statistics behind the models and interpret, extrapolate, and prescribe from data to deliver actionable recommendations using effective visualizations


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