Data Scientist Level 3

IntelliGenesis•Columbia, MD

29d•$109,000 - $149,000

About The Position

This role involves employing a combination of foundational, data processing, and modeling skills to extract meaning and value from large datasets. The Data Scientist will devise strategies, make and communicate principled conclusions using mathematics, statistics, computer science, and application-specific knowledge. They will develop and implement qualitative and quantitative methods for characterizing, exploring, and assessing large datasets, translating mission needs into technical requirements, and communicating complex technical information to non-technical audiences. The position also requires staying aware of evolving collection, processing, storage, and analytic capabilities to make informed recommendations on technical solutions.

Requirements

Must be a U.S. Citizen
Active TS/SCI clearance with polygraph required
Minimum of eight (8) years of relevant experience and a Master's degree; ten (10) years of relevant experience and a Bachelor’s degree or 12 years of relevant experience and an Associate’s degree required.
Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science.
Relevant experience must be two or more of the following: 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, Software engineering.

Nice To Haves

Information Assurance Certification may be required.

Responsibilities

Employ a combination of at least two skill areas: Foundations (Mathematical, Computational, Statistical), Data Processing (Data management and curation, data description and visualization, workflow, and reproducibility), and Modeling, Inference, and Prediction (Data modeling and assessment, domain-specific considerations).
Devise strategies for extracting meaning and value from large datasets.
Make and communicate principled conclusions from data using elements of mathematics, statistics, computer science, and application-specific knowledge.
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 features and limitations inherent in customer data holdings through analytic modeling, statistical analysis, programming, and/or other appropriate scientific method.
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.
Make informed recommendations regarding competing technical solutions by maintaining awareness of constantly shifting collection, processing, storage and analytic capabilities and limitations.