DATA SCIENTIST III - MARLAND - URGENT

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