SCI w Poly

Peraton•Laurel, MD

20d•Onsite

About The Position

Designs, develops, tests, and operationalizes algorithms to solve complex business challenges and mission requirements. Models are built to learn from data and generate predictions aligned with customer-defined business logic and operational needs. Demonstrates proficiency across model architecture, data pipeline integration, and the application, interpretation, and presentation of performance metrics. Maintains a strong understanding of foundational concepts in application development, infrastructure management, data engineering, and data governance. Responsible for training, retraining, deploying, scheduling, monitoring, and continuously improving models based on iterative user and system feedback to deliver scalable, high-performance solutions. May lead geographically distributed teams and frequently serve as the primary point of contact for related technical matters, requiring strong analytical thinking, problem-solving ability, and exceptional communication skills.

Requirements

Minimum of 5 years of experience in applied machine learning on programs/contracts of similar scope, type, and complexity.
Master’s or Ph.D. degree in Advanced Mathematics, Artificial Intelligence, Data Science, Computer Science, or Deep Learning from an accredited college or university.
A relevant Bachelor’s degree plus 5 additional years of machine learning experience may be substituted for a Master’s degree.
An active TS/SCI with Poly is required.

Nice To Haves

Expert knowledge of multiple programming languages, e.g. Python, Java, C, R, is desired.

Responsibilities

Designs, develops, tests, and operationalizes algorithms to solve complex business challenges and mission requirements.
Builds models to learn from data and generate predictions aligned with customer-defined business logic and operational needs.
Demonstrates proficiency across model architecture, data pipeline integration, and the application, interpretation, and presentation of performance metrics.
Maintains a strong understanding of foundational concepts in application development, infrastructure management, data engineering, and data governance.
Responsible for training, retraining, deploying, scheduling, monitoring, and continuously improving models based on iterative user and system feedback to deliver scalable, high-performance solutions.
May lead geographically distributed teams and frequently serve as the primary point of contact for related technical matters, requiring strong analytical thinking, problem-solving ability, and exceptional communication skills.
Experience working with various ML libraries, packages, and frameworks.
Experience with standard machine learning frameworks such as: PyTorch or TensorFlow.
Design or select appropriate data and knowledge representation methods.
Recognize and apply software architecture, data modeling, and data structure principles.
Transform data science prototypes into scalable production solutions.
Provide system integration oversight.
Select appropriate datasets for machine learning applications.
Perform statistical analysis and data interpretation.
Run and evaluate machine learning algorithms.
Use analytical results to improve and optimize models.
Train and retrain systems as required.
Research and implement a broad range of AI/ML algorithms and tools.
Verify data integrity and model output quality.
Identify data distribution differences that may impact model performance.
Run standard testing and evaluation protocols.
Oversee testing and evaluation of AI/ML algorithms through iterative design processes.
Ensure solutions meet verification and validation requirements.