Data Scientist Security Clearance

Position: Data Scientist 2 with Security Clearance
At Wyetech, you'll be at the center of an award-winning corporate culture, breaking technological barriers and solving real-world problems for our federal government customers. We are committed to hiring the best of the best, and in return, we offer a world-class, truly unique employee experience that is rare within our industry. 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.

Due to federal contract requirements, United States Citizenship and position appropriate security clearance is required. (e.g. Active TS/SCI security clearance with agency appropriate polygraph). Capabilities
* 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

Required Qualifications
* TS/SCI with agency appropriate poly

* 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. Required Technical Skills
* 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 Num Py 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 Tensor Flow 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 Postgre

SQL and Mongo

DB) 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. The Benefits Package
* Wyetech believes in…