Senior Data Scientist

Parsons Corporation•Aberdeen, MD

4d•$112,200 - $196,400

About The Position

Parsons is seeking a Senior Data Scientist to support our cutting-edge Drone Armor counter-unmanned aerial systems (C-UAS) program. This role provides professional scientific data engineering and data science, requiring the application of data and engineering sciences, mathematics, and electronic phenomena to design, implement, and optimize data-centric capabilities. The Senior Data Scientist will focus on software coding, data interoperability, cloud technology, data mesh, and data tagging to enable advanced analytics, decision-support, and mission effectiveness.

Requirements

Master’s degree in Computer Science, Electronics Engineering, or other engineering or technical discipline is required OR 8 years of relevant experience in data science, data engineering, or related fields may be substituted for education
Experience applying data and engineering sciences, mathematics, and electronic phenomena to real-world systems or mission problems
Experience designing and implementing data pipelines and data interoperability solutions in complex, multi-system environments
Experience with cloud-based or hybrid data and analytics solutions, including data storage, processing, and model deployment
Experience working with data mesh or similar distributed data architectures, including data product design and governance
Experience with data tagging, metadata management, and data cataloging to support discoverability, security, and compliance
Experience reacting to and resolving data-related issues (e.g., quality, latency, integrity, model performance) in complex or mission-critical systems
Proficiency in one or more languages commonly used for data science and data engineering (e.g., Python, R, Scala, or similar), including use of relevant libraries and frameworks
Strong understanding of data engineering concepts: ETL/ELT, streaming, batch processing, APIs, and event-driven data flows
Familiarity with cloud data and analytics services (e.g., managed databases, data lakes, streaming services, containerized processing)
Strong analytical and communication skills, capable of explaining complex data and model behavior, trade-offs, and limitations to both technical and non-technical stakeholders
Must be a US Citizen
Ability to obtain and maintain a security clearance (SECRET or higher; specific level may be defined by program requirements)

Nice To Haves

Knowledge of data modeling, schemas, and standards used in sensor, RF, telemetry, or ISR/C2 environments is a plus
Master’s or higher degree in Data Science, Computer Science, Electrical/Electronics Engineering, or related discipline
Professional certifications in cloud platforms, data engineering, or data science/ML
Experience supporting DoD, defense, ISR, C-UAS, or other mission/operations-focused data and analytics systems
Experience working with multi-sensor or multi-int data fusion, track management, or situational awareness systems
Experience implementing data mesh or data fabric strategies in distributed, real-time, or high-availability environments
Experience with data processing and orchestration tools (e.g., Airflow, NiFi, Spark, Flink, or similar)
Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD workflows for data and model deployment
Familiarity with Agile/Scrum methodologies and modern issue tracking/ALM tools
Experience with visualization and BI tools (e.g., Grafana, Kibana, Power BI, Tableau, or similar) for operational and analytic dashboards

Responsibilities

Design and implement data science workflows and analytics to support C-UAS detection, tracking, classification, and decision-support
Develop and apply statistical, machine learning, and optimization techniques to extract insight from multi-source sensor, RF, telemetry, and operational data
Build, evaluate, and refine models for anomaly detection, threat assessment, and system performance prediction in real-time or near-real-time environments
Translate mission and system requirements into data-driven approaches, metrics, and analytic products consumable by operators, engineers, and leadership
Design and implement data pipelines for ingestion, transformation, enrichment, and distribution of data across a data mesh architecture
Engineer data interoperability solutions across heterogeneous systems, sensors, and platforms, using common data models, schemas, and open standards where appropriate
Collaborate on the design and implementation of a data mesh or data fabric for Drone Armor, enabling discoverable, shareable, and governed data products across teams and systems
Ensure data solutions are robust, secure, maintainable, and aligned with program architecture, performance, and interoperability standards
Architect and implement data pipelines, storage, and analytics capabilities in cloud or hybrid environments (e.g., commercial, tactical, or private cloud)
Leverage cloud-native services and technologies for scalable data processing, streaming, and model deployment
Optimize data and analytics workloads for resilience, performance, and cost within cloud-based infrastructures
Integrate on-premise, edge, and cloud components into cohesive end-to-end data and analytics solutions for mission operations
Design and implement data tagging strategies (e.g., metadata, security labels, lineage tags) to support discoverability, access control, and policy compliance
Define and enforce data quality metrics, validation rules, and monitoring to ensure the reliability of analytics and downstream decision-making
Work with stakeholders to establish data governance practices, including data cataloging, classification, and lifecycle management
Support the creation of well-documented, reusable data products and datasets within the data mesh
Work closely with systems engineers, RF engineers, software developers, and operators to understand mission needs and translate them into data and analytics requirements
Develop visualizations, dashboards, and analytic reports that clearly communicate complex findings to both technical and non-technical audiences
Mentor junior data scientists and data engineers, providing technical guidance on methods, tools, and best practices
Contribute to technical reviews, design walkthroughs, and continuous improvement of data science and data engineering practices