Data Scientist- Mid

Required Qualifications:

• A Bachelor’s degree in a related field with 5+ years of relevant experience; a Master's degree in a related field with 3+ years of relevant experience; a PhD in a related field with 0+ years of relevant experience; OR an additional 4 years of experience maybe considered in lieu of a degree requirement
• Knowledge of Python or R
• Possesses knowledge of advanced analytics and data storage
• Demonstrated oral and written communication skills
• Knowledge of various data science applications and architectures
• Active TS adjudication with ability to obtain the SCI and CIP.

Preferred Qualifications:

• Polished oral and written communication skills sufficient to compose, tailor, and deliver original presentations and papers on abstract concepts
• Develops and employs strategies to analyze key issues, draw conclusions from data, and recommend viable solutions to address customer mission needs.
• Ability to lead multi-disciplinary teams to complete complex data science projects.
• Experience with AWS or similar cloud provider
• Demonstrated experience in solving problems with structured and unstructured data
• Experienced in applying statistical and data visualization skills
• Strong problem solving skills to manipulate data and draw insights from large data sets
• Proven ability to learn and master new software, technologies and techniques
• Experience in two or more of the following: Python, Machine Learning, Natural Language Processing, Neural Networks, Apache Spark, Hadoop, R, C++, SQL Database/Coding, or visualization tools such as Tableau

Our program supports unclassified and classified software development and integration services. Some of the work we will do is brand new Big Data, data analysis, Artificial Intelligence (AI), Machine Learning (ML), and advanced analytics, while other work will be refining and improving legacy development, as well as providing operations and maintenance

Position Responsibilities:

Work with customer’s Chief Technology Office to further application and understanding of data science. Apply technical methods to data science problems supporting business and mission users. Advise or lead interdisciplinary teams throughout the full course of a data science project life cycle. Understands Machine Learning and is able to apply machine learning, multivariable calculus, and linear algebra techniques and approaches, including but not limited to, k-nearest neighbors, random forests, and ensemble methods. Understands Data Visualization and is able to employ visualization and data to enable data driven decisions.