Data Scientist Level 2

IntelliGenesis•Columbia, MD

87d•$85,000 - $116,000

About The Position

This role involves employing a combination of mathematical, computational, and statistical skills, along with data processing, modeling, inference, and prediction expertise. The Data Scientist will devise strategies for extracting meaning and value from large datasets, making and communicating principled conclusions using elements of mathematics, statistics, computer science, and application-specific knowledge. The position requires developing and implementing 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 role also involves making informed recommendations regarding competing technical solutions by staying aware of evolving collection, processing, storage, and analytic capabilities and limitations.

Requirements

Must be U.S. Citizen
Active TS/SCI clearance with polygraph required
Minimum of three (3) years of relevant experience and a Bachelor’s degree or five (5) 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.
A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university.
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 mathematical, computational, and statistical skills.
Utilize data processing techniques including data management and curation, data description and visualization, workflow, and reproducibility.
Apply modeling, inference, and prediction methods, including data modeling and assessment, and 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.
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.