Data Scientist

Leidos•Chantilly, VA

14d•Onsite

About The Position

Leidos is seeking a Senior Data Scientist to support an upcoming program in Chantilly, VA. This role will provide senior-level expertise in the design, development, integration, and analysis of complex data environments. The successful candidate will develop methods, processes, and systems to consolidate, transform, manage, and analyze structured and unstructured data from diverse and disparate sources. The Senior Data Scientist will work across applications and data environments to identify analytical data requirements, improve data quality and accessibility, develop analytical solutions, and enable the effective use of data for operational and mission needs. The role requires the ability to understand complex data flows and systems, identify issues and dependencies, and translate technical data challenges into actionable solutions for both technical teams and leadership.

Requirements

Bachelor’s degree and 12+ years of relevant experience, or Master’s degree with 10+ years of experience. Additional years of experience may be considered in lieu of a degree.
6+ years of experience managing, integrating, or troubleshooting data flows between applications and/or systems.
Strong understanding of how application data is generated, managed, transformed, transferred, and consumed across interconnected systems.
Demonstrated experience analyzing complex technical problems and developing practical, data-driven solutions.
Experience identifying analytical data requirements and determining appropriate data sources, structures, and processes to support those requirements.
Experience with dataset management, data quality, data integration, and associated processes.
Understanding of software development and CI/CD pipelines and how data processes integrate with application development and deployment environments.
Understanding of how machine learning and artificial intelligence capabilities utilize, process, and derive value from data.
Active TS/SCI clearance with Polygraph is required.
US Citizenship is required.

Nice To Haves

Experience with ServiceNow, particularly ITSM and CMDB.
Experience with Splunk or other enterprise data/logging platforms.
Experience with Cameo or other systems engineering/model-based systems engineering (MBSE) tools.
Experience developing reports, dashboards, data visualizations, or other analytical products for leadership and operational stakeholders.
Experience with data engineering, ETL/ELT processes, APIs, data pipelines, or application-to-application data integration.
Experience working with cloud, distributed, or enterprise-scale data environments.
Experience supporting data analytics, machine learning, artificial intelligence, or other advanced data-driven capabilities.
Experience documenting data architectures, data flows, interfaces, and system dependencies.

Responsibilities

Design, develop, and implement methods and processes to collect, integrate, transform, manage, and analyze large and complex datasets from disparate sources.
Develop and apply advanced statistical concepts, algorithms, queries, and automated processes to clean, model, integrate, and evaluate structured and unstructured data.
Analyze complex data flows between applications and identify opportunities to improve data quality, integrity, accessibility, and usability.
Identify analytical data requirements and develop solutions to support operational, business, and mission objectives.
Evaluate data structures, sources, interfaces, and dependencies to identify gaps, inconsistencies, and opportunities for improvement.
Collaborate with software engineers, data engineers, system administrators, analysts, and other technical stakeholders to integrate and leverage data across applications and environments.
Support the development and integration of ML/AI capabilities by understanding data requirements, data preparation, and the use of data throughout the ML/AI lifecycle.
Develop and maintain automated data processes and support data integration within CI/CD environments.
Investigate complex data and system issues, determine root causes, and develop practical solutions.
Communicate technical findings, data issues, risks, and recommended solutions clearly to technical teams and program leadership.
Contribute to data standards, governance, documentation, and dataset management processes.