Data Scientist 2 - FFPP-8757

TO BE CONSIDERED FOR THIS POSITION YOU MUST CURRENTLY HAVE AN ACTIVE TS/SCI WITH POLYGRAPH SECURITY CLEARANCE WITH THE FEDERAL GOVERNMENT. (U.S. CITIZENSHIP REQUIRED).

Requirements:
This position requires a poly, within 7 years.

- Programming Languages: Proficiency in programming languages such as Python and R is crucial for data manipulation, analysis, and implementing algorithms. Python is favored for its simplicity and extensive libraries (like NumPy and pandas), while R is preferred for statistical analysis and data visualization.

- Statistical Analysis: A strong foundation in statistics and probability is necessary for analyzing data accurately and making informed decisions. Understanding concepts like regression analysis, hypothesis testing, and statistical distributions is essential.

- Machine Learning: Knowledge of machine learning algorithms and frameworks (such as TensorFlow and Scikit-Learn) is vital for building predictive models and automating decision-making processes.

- Data Wrangling: The ability to clean and organize complex datasets is critical. Data wrangling involves transforming raw data into a usable format, which is often time-consuming but necessary for effective analysis.

- Database Management: Familiarity with SQL and database management systems (like PostgreSQL and MongoDB) is essential for extracting and manipulating data stored in relational databases.

- Data Visualization: Skills in data visualization tools (such as Tableau and Matplotlib) help communicate findings effectively. Creating charts, graphs, and dashboards is crucial for making data understandable to stakeholders.

Description:

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.

Bachelor's degree from an accredited college or university in a quantitative discipline (e.g., statistics, mathematics, operations research, engineering or computer science). Five (5) years of experience analyzing datasets and developing analytics, five (5) years of experience programming with data analysis software such as R, Python, SAS, or MATLAB. An additional four (4) years of experience in software development, cloud development, analyzing datasets, or developing descriptive, predictive, and prescriptive analytics can be substituted for a Bachelor's degree. A PhD from an accredited college or university in a quantitative discipline can be substituted for four (4) 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) 
Oversee the development of individual analytic efforts and guide team in analytic development process 
Guide analytic development toward solutions that can scale to large datasets
Partner with software engineers and cloud developers to develop production analytics 
Develop and train machine learning systems based on statistical analysis of data characteristics to support mission automation

Compensation:
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