Data Scientist III

Job Number: 43 Job Category: GovTech Job Title: DATA SCIENTIST III - MARYLAND - URGENT Job Type: Full-time Clearance Level: Top secret/SCI Top Secret/SCI - Full Scope Polygraph Work Arrangement: On-site Job Location: Ft Meade MD Salary: 200k - 250k Background Devise strategies for extracting meaning and value from large datasets 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, accounting for the unique features and limitations of data holdings
Translate practical mission needs and analytic questions related to large datasets into technical requirements, and assist others in drawing appropriate conclusions from the analysis of such data
Effectively communicate complex technical information to non-technical audiences and make informed recommendations on competing technical solutions Requirements Bachelor's Degree in Mathematics, Applied Mathematics, Statistics, Data Science, Machine Learning, Operations Research, Computer Science, or a related field
Degrees in physical sciences (e.g., physics, chemistry) with a computational focus or a Certificate in Data Science from an accredited institution 10 years of relevant experience in designing and implementing machine learning, data science, advanced analytics, statistical analysis (e.g., hypothesis testing, linear models), and programming (e.g., Python)
Experience in data management (e.g., data cleaning, transformation), data mining, data modeling, artificial intelligence, and/or software engineering, with expertise in at least 3 of these areas
Must have Top Secret/SCI - Polygraph clearance Preferred Accurately and automatically tokenize language data with spoken or written origins
Develop automated solutions for the annotation of language data with parts of speech information, and improved existing models by scoring performance against human-generated annotations for speech and text
Demonstrated NLP experience