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Data Scientist (Level 2)
Learn more about this opportunity below: REQ ID: TKO - Data Scientist (Level 2)
Clearance: TS/SCI FSP
Location: Ft. Meade
Bachelor's and Master's degree from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science). Five years of experience analyzing datasets and developing analytics, five years of experience programming with data analysis software such as R, Python, SAS, or MATLAB. An additional two years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Master's degree. A PhD from an accredited college or university in a quantitative discipline can be substituted for three years of experience.
- Produce data visualizations that provide insight into dataset structure and meaning
- Work with subject matters experts (SMEs) to identify important information in raw data and develop scripts that extract this information from a variety of data formats (e.g., SQL tables, structured metadata, network logs)
- Incorporate SME input into feature vectors suitable for analytic development and testing
-Translate customer qualitative analysis process and goals into quantitative formulations that are coded into software prototypes
- Develop and implement statistical, machine learning, and heuristic techniques to create descriptive, predictive, and prescriptive analytics
- Develop statistical tests to make data-driven recommendations and decisions
- Develop experiments to collect data or models to simulate data when required data are unavailable
- Develop feature vectors for input into machine learning algorithms
- Identify the most appropriate algorithm for a given dataset and tune input and model parameters
- Evaluate and validate the performance of analytics using standard techniques and metrics (e.g. cross validation, ROC curves, confusion matrices)
A data scientist will develop machine learning, data mining, statistical and graph-based algorithms to analyze and make sense of datasets; prototype or consider several algorithms and decide upon final model based on suitable performance metrics; build models or develop experiments to generate data when training or example datasets are unavailable; generate reports and visualizations that summarize datasets and provide data-driven insights to customers; partner with subject matter experts to translate manual data analysis into automated analytics; implement prototype algorithms within production frameworks for integration into analyst workflows.