Data Scientist Level 2

DigiFlight•Columbia, MD

15h•Onsite

About The Position

Join an outstanding team that offers exciting job opportunities with the goal of providing the absolute best support to our customers. Here at DigiFlight we embrace integrity, innovative solutions, put our customers first and offer a highly competitive benefits package! Role description: Data Scientist Level 2: Description: Employ some combination (2 or more) of the following skill areas: I. Foundations: (Mathematical, Computational, Statistical) 2. Data Processing: (Data management and curation, data description and visualization, workflow, and reproducibility) 3. 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. Through 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 features and limitations inherent in NSA/CSS data holdings. Translate practical mission needs and analytic questions related to large datasets into technical requirements and, conversely, assist others in withdrawing 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 the constantly shifting NSA/CSS collection, processing, storage, and analytic capabilities and limitations.

Requirements

Bachelor’s Degree with 3 years of relevant experience OR Associate’s degree with 5 years of relevant experience.
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.
Bachelor's Degree must be in Mathematics, Applied Mathematics Statistics, Applied Statistics, Machine learning, Data Science, Operations Research, or Computer Science or a degree in a related field (Computer Information Systems, Engineering), a degree in the physical/hard sciences (e.g. physics, chemistry, biology, astronomy), or other science disciplines with a substantial computational component (i.e. behavioral, social, or life) may be considered if it included a concentration of coursework (5 or more courses) in advanced Mathematics (typically 300 level or higher, such as linear algebra, probability and statistics, machine learning) and/or computer science (e.g. algorithms, programming, data structures, data mining, artificial intelligence).
College-level requirements or upper-level math courses designated as elementary or basic do not count.
A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university.

Nice To Haves

Experience in more than one area is strongly preferred.

Responsibilities

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 NSA/CSS data holdings.
Translate practical mission needs and analytic questions related to large datasets into technical requirements.
Assist others in withdrawing 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 the constantly shifting NSA/CSS collection, processing, storage, and analytic capabilities and limitations.