Machine Learning / Data Scientist

Parsons Corporation•Washington, DC

1d•$88,400 - $154,700•Onsite

About The Position

We have a career opportunity for a Machine Learning / Data Scientist to develop advanced analytical models and experiments that enhance decision-making, improve forecasting, and uncover insights across mission and support activities. This role would be based in Washington, DC. Would be required to be on-site This role will support the enablement of machine learning capabilities within our analytics environment, working closely with data analysts, engineers, and program stakeholders. The Machine Learning / Data Scientist is a hands-on practitioner with strong capabilities in model development, data preparation, and analytical storytelling. This role requires the ability to frame complex problems, select appropriate modeling approaches, implement and validate models, and communicate results in accessible terms to non-technical audiences.

Requirements

Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, or a closely related field; a master’s degree is preferred but not required, or equivalent work experience.
5+ years of experience in data science, machine learning, or advanced analytics roles.
Demonstrated experience developing, validating, and deploying machine learning models using tools such as Python, R, or equivalent.
Strong background in statistics, model evaluation, and experimental design.
Experience with data preparation, feature engineering, and working with complex, multi-source datasets.

Nice To Haves

Familiarity with integrating model outputs into BI tools or applications (e.g., via APIs, embedded analytics) is preferred.
Experience working within mission-oriented or public sector environments (e.g., DHS, DoD) is a plus.

Responsibilities

Design and implement machine learning and advanced analytics solutions that address operational, programmatic, or strategic questions.
Collaborate with stakeholders to define analytical problems, identify relevant data, and translate business needs into modeling requirements.
Prepare and engineer features from multiple data sources, ensuring data quality and suitability for modeling.
Develop, train, and validate models (e.g., classification, regression, clustering, forecasting) using appropriate techniques and tools.
Evaluate model performance, perform error analysis, and refine approaches to improve accuracy, robustness, and interpretability.
Integrate model outputs into dashboards, applications, or automated workflows, in coordination with analytics and development teams.
Document modeling approaches, assumptions, and results, and communicate findings through clear narratives and visualizations.
Support experimentation and pilot projects that explore new analytical techniques and tools.
Contribute to the development of standards and practices for responsible and sustainable use of advanced analytics.