poly

Peraton•Laurel, MD

17h•$146,000 - $234,000•Onsite

About The Position

The 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. Other duties may include: Oversee one or more software development teams and ensure the work is completed in accordance with the constraints of the software development process being used on any project. 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. Develop Al and machine learning models to address complex problems. Develop and optimize Large Language Models (LLM) for various NLP tasks and information retrieval. 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.

Requirements

Bachelor's degree from an accredited college or university in quantitative discipline (e.g., statistics, mathematics, operations research, engineering, or computer science) and five (5) years of experience analyzing datasets and developing analytics using 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 Master's degree in an accredited college or university can be substituted for two (2) years of experience
A PhD from an accredited college or university in a quantitative discipline can be substituted for four (4) years of experience
Experience with utilizing GPU-based computing resources to accelerate model training and deployment
Active TS/SCI security clearance with a current polygraph is required

Nice To Haves

Designing/implementing machine learning, data science, and advanced analytical algorithms capabilities developed in at least one high-level programming language (e.g. Python)
Statistical analysis (e.g. variability, sampling error, inference, hypothesis testing, EDA, application of linear models)
Data management (e.g. data cleaning and transformation), data mining, data modeling and assessment, artificial intelligence, and/or software engineering

Responsibilities

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
Oversee one or more software development teams and ensure the work is completed in accordance with the constraints of the software development process being used on any project
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
Develop Al and machine learning models to address complex problems
Develop and optimize Large Language Models (LLM) for various NLP tasks and information retrieval
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