Data Scientist - Clearance Required

Key Skills

C++, Clustering, Data science techniques, Flax Programming Language, Java Programming, Machine learning techniques, Python Programming, R Programming, SQL

Job Description

LMI has been named a 2022 #TopWorkplace in the United States by Top Workplaces!

We are honored to be recognized as a company that values a people-centered culture, and we are grateful to our employees for making this possible!

LMI is seeking an experienced an experienced Data Scientist. This position may be remote.

*Active Top Secret/SCI clearance required for this position. This position is on-site in Springfield, VA*

Responsibilities

Responsibilities may include:

• 3+ years applying statistics, machine learning and analytic approaches to answer key intelligence questions, producing results and re-usable products that provide value to Intelligence Analysts.
• Work with analysts to interpret analytic results and refine analysis as needed. Collaborate with Intelligence Analysts to understand needs and devise possible solutions.
• Apply machine learning techniques to identify and transform data features, find hidden patterns and trends, build and optimize classifiers, create automated anomaly detection systems, and automate steps of the intelligence process.
• Communicate results and ideas to key decision makers, analysts and other stakeholders. Contractor shall advise on the interpretation and use of data intelligence products, quality assessments and applications.

Qualifications

QUALIFICATIONS:

• Active TS//SCI clearance
• MS in Data Science, Statistics, Applied Math, or related field preferred.
• 5+ years of related experience required
• Outstanding communication skills, written and verbal
• Highly organized and able to manage multiple projects simultaneously
• Team-player mentality with a positive attitude
• Keen attention to detail and solid analytical skills

Languages, Tools, and Techniques

• Demonstrated proficiency with Python and SQL to manipulate data and draw insights from large data sets.
• Demonstrated proficiency in a variety of supervised and unsupervised machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Demonstrated proficiency in advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
• Demonstrated proficiency with common data science libraries.
• Demonstrated proficiency visualizing/presenting data for stakeholders.
• Capability to recognize when Data Science can add value to an intelligence problem, and when it cannot.