Data Scientist 2

Gormat•Annapolis Junction, MD

20h

About The Position

We are seeking a Data Scientist with a background in AI, Machine Learning (ML), and Natural Language Processing (NLP). You will triage the development of large language datasets and develop tools and techniques for analysis. The focus of this position will be on rapid prototyping and data mining. The Level 2 Data Scientist shall possess the following capabilities: Foundations: (Mathematical, Computational, Statistical). Data Processing: (Data management and curation, data description and visualization, workflow and reproducibility). Modeling, Inference, and Prediction: (Data modeling and assessment, domain-specific considerations). Ability to make and communicate principal conclusions from data using elements of mathematics, statistics, computer science, and applications-specific knowledge. Ability to use analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique feature and limitations inherent in Customer data holdings. Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others with drawing appropriate conclusions from the analysis of such data. Effectively communicate complex technical information to non-technical audiences. AI/ML experience to impact and assess large datasets. This includes data modeling, computational mathematics, qualitative and quantitative techniques, data visualizations and AI/ML model development and deployment. Advanced statistical and predictive modeling. Large-scale data processing (e.g., Spark, cloud data platforms). Providing advanced discovery support utilizing machine learning, analytical prototyping, scripting, automation, data visualization, statistical analysis, and TechSIGINT tools. Solution needs be adaptable to new tools and technologies as needed.

Requirements

Foundations in Mathematics, Computation, and Statistics
Data management and curation
Data description and visualization
Workflow and reproducibility
Data modeling and assessment
Domain-specific considerations
AI/ML experience
Advanced statistical and predictive modeling
Large-scale data processing (e.g., Spark, cloud data platforms)
Bachelor's Degree with 3 years of relevant experience
Associate's degree with 5 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position
Relevant experience must be in designing/implementing machine learning, data science, advanced analytical algorithms, programming (skill in at least one high-level language (e.g. Python)), statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models), data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering
TS/SCI with polygraph is required

Nice To Haves

Certificate in Data Science from an accredited college/university (if degree is not in a specified field)

Responsibilities

Triage the development of large language datasets
Develop tools and techniques for analysis
Rapid prototyping and data mining
Make and communicate principal conclusions from data using elements of mathematics, statistics, computer science, and applications-specific knowledge
Use analytic modeling, statistical analysis, programming, and/or another appropriate scientific method, develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets in various states of organization, cleanliness, and structure that account for the unique feature and limitations inherent in Customer data holdings
Translate practical mission needs and analytic questions related to large datasets into technical requirements
Assist others with drawing appropriate conclusions from the analysis of such data
Effectively communicate complex technical information to non-technical audiences
Impact and assess large datasets using AI/ML experience
Provide advanced discovery support utilizing machine learning, analytical prototyping, scripting, automation, data visualization, statistical analysis, and TechSIGINT tools
Adapt solutions to new tools and technologies as needed