Data Scientist - AI/ML Senior Engineer (TS/SCI with Poly Required)

GCI embodies excellence, integrity, and professionalism.  The employees supporting our customers deliver unique, high-value mission solutions while effectively leveraging the technological expertise of our valued workforce to meet critical mission requirements in the areas of Data Analytics and Software Development, Engineering, Targeting and Analysis, Operations, Training, and Cyber Operations.  We maximize opportunities for success by building and maintaining trusted and reliable partnerships with our customers and industry.

At GCI, we solve the hard problems.  As a Data Scientist, a typical day will include the following duties:

JOB DESCRIPTION

The Data Scientist will be a member of a small team tasked with initiating an AI/ML initiative for the customer.  The ideal Data Scientist will have a background in AI/ML Engineer that can develop algorithms, write scripts, build predictive analytics, use automation, apply machine learning, and use the right combination of tools and frameworks to turn a set of data points into objective answers to help senior leadership make informed decisions. The Data Scientist will apply data mining techniques perform statistical analysis, and build high-quality prediction models. The Data Scientist’s ability to obtain data through advanced computerized models and extrapolation of data patterns through advanced algorithms to explain how the information will influence the specific project will be an essential function to providing customers a better comprehension of the data.

This candidate will work closely with customer management, other project managers, system architects, data scientists, data engineers, and machine learning engineers to formulate recommendations for enhancements and improvements geared towards utilizing innovative engineering solutions.

REQUIRED KNOWLEDGE/SKILLS

1. BS/BA in Software Engineering, Science, Mathematics, or similar OR equivalent combination of education and experience
2. Familiarization with Large Language Model (LLM) architectures and training procedures
3. Experience with search architecture tools (ex - Solr, ElasticSearch)
4. Big Data Frameworks such as Spark or Hadoop
5. Data Science frameworks such as Keras, Tensorflow, or Theano
6. Experience with building querying ontologies such as Zeno, OWL, RDF, SparQL
7. Experience with GPU processing
8. Experience with word2vec or doc2vec algorithms
9. Proficient in Java and Python
10. Must know to use an IDE to code (ex – Visual Studio Code, IntelliJ, Eclipse, NetBeans)
11. Experience with interacting in a Linux environment (ex - Bash scripting, VI)
12. Intrapersonal communication skills
13. Ability to work well in a constantly evolving work environment

DESIRED KNOWLEDGE/SKILLS

1. Familiarity with Source code management and integration (ex - GitHub/GitLab, Jenkins)
2. Hands on experience with cloud technology (AWS / C2S)
3. Experience in an Agile environment
4. Experience with testing frameworks (ex - Junit, Mockito, Swagger, Postman)
5. Experience with relational databases (ex - Oracle / MySql)
6. Experience with JavaScript / Typescript
7. Experience with front end development (ex - Angular 2+, React, HTML, CSS, JQuery)

KEY RESPONSIBILITES

1.  Develop and train Large Language Models (LLMs) in support of the customer mission
2.  Investigate business area processes for creative implementation of LLMs
3. Apply data mining techniques to perform statistical analysis
4. Assists with technical planning activities, including roadmap development and systems integration.
5. Collaborates with a variety of customers and contractors on a regular basis, which may include technical consultation, meeting coordination and support (e.g., TEMs), and preparation/support of technical briefings.
6. Participates in the development of technical project plans, reports, and contract (e.g., PMR) briefings.

 The ideal candidate will work closely with data scientists, analysts, and customer stakeholders to create and deploy new product features. The AI/ML Engineer will establish scalable, efficient, automated processes for data analyses, model development, validation and implementation.