Clearance:
Active TS/SCI and Full Scope Polygraph Clearance Required (Expired within 18 Months is also acceptable)
About the Role:
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.
Responsibilities:
Produce data visualizations that provide insight into dataset structure and meaning.
Collaborate 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).
Evaluate individual analytic efforts and make recommendations in the analytic development
process.
Recommend solutions that can scale to large datasets.
Collaborate with software engineers, cloud developers, and appropriate stakeholders to develop production analytics.
Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation.
Requirements
Required Education & Years of Experience:
- Requires a Bachelor's degree in a relevant discipline (e.g., statistics, mathematics, operations research, engineering, or computer science) from an accredited college or university, eight (8) years of relevant experience analyzing datasets and developing analytics, and five (5) years of relevant experience programming with data analysis software such as R, Python, SAS, or MATLAB.
- A Master's degree in relevant discipline may be substituted for two (2) years of relevant experience reducing the requirement to six (6) years of relevant experience analyzing datasets and developing analytics, and three (3) years of relevant experience programming with data analysis software such as R, Python, SAS, or MATLAB.
- A PhD in relevant discipline may be substituted for four (4) years relevant experience reducing the requirement to four (4) years of relevant experience analyzing datasets and developing analytics, and one (1) year of experience programming with data analysis software such as R, Python, SAS, or MATLAB.
- In lieu of a Bachelor’s Degree, an additional four (4) years of relevant experience may be substituted for a total of twelve (12) years of relevant experience analyzing datasets and developing analytics, and nine (9) years of experience programming with data analysis software such as R, Python, SAS, or MATLAB.