Data Scientist

Description

Founded in 2007 and headquartered in Columbia, Maryland, Synergy ECP is a leading provider of cybersecurity, software and systems engineering and IT services to the U.S. intelligence and defense communities. The company leverages its expertise in data transport solutions, software and systems engineering, and other solutions to deliver critical and innovative capabilities to high-level decision makers that enhance our nation’s security.

In an ultra-competitive environment, Synergy ECP has thrived by adhering to our name, making sure excellence is displayed by our Employees, to our Customers and by Improving Performance (ECP).

It’s what sets us apart, enabling us to be an autonomous yet agile business that delivers huge results - showing we’re ready to meet our customers’ evolving demands.

Synergy ECP has earned a client list that includes numerous Fortune 100 companies, in addition to multiple branches of the US government and military services.

Synergy ECP is an equal opportunity employer and considers qualified applicants for employment without regard to race, color, creed, religion, national origin, sex, sexual orientation, gender identity and expression, age, disability, veteran status, or any other protected class.

Clearance Required: TS/SCI w/ Polygraph

• 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. Experience in more than one area is strongly preferred.

• Employ some combination (2 or more) of the following areas: 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)

• 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 to NSA/CSS 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.

• 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

A Bachelor’s degree and 3 years of relevant experience. An Associate’s degree plus 5 years of relevant experience may be considered for individuals with in-depth experience that is clearly related to the position.

Degree must be in Mathematics, Applied Mathematics, Statistics, Applied Statistics, Machine Learning, Data Science, Operations Research, or Computer Science. A degree in a related field (e.g., 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, and life) may be considered if it includes a concentration of coursework (typically 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., algorthms, programming, data structures, data mining, artificial intelligence). College-level Algebra or other math courses intended to meet a basic college level requirement, or upper level math courses designated as elementary or basic do not count.

Note: A broader range of degrees will be considered if accompanied by a Certificate in Data Science from an accredited college/university.