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Data Scientist


Summary

This position is located in the Air Traffic Organization (ATO); Technical Operations (AJW), NAS Policy and Quality Control Group (AJW-1800). This Group is responsible for NAS operations quality control oversight, policy oversight, and enterprise configuration management for all of Technical Operations.

Duties

This position will work with engineering, data, and analytics teams from across the Air Traffic organization and will be responsible for helping to develop data analysis capabilities to help with decision-making and maintenance optimization. This is a high-profile position that will work with cross-functional teams to derive information from data to solve previously unsolvable problems. This role requires strong communication skills, bridging functional areas to ensure that executives, strategy leaders, data analysts, and engineers are all moving cohesively toward the same tangible goals.

The Data Scientist performs multiple, varying, and complex assignments under the minimal direction of a manager, project/program manager, team leader, or more experienced technical specialist. Applies experience and comprehensive knowledge of computer science and mathematical and statistical theories, techniques, and methods to gather, analyze, design, and construct new processes for modeling, interpreting, and/or reporting quantitative information, trends, relationships and correlations among or within data sets. The work activities typically impact directly on the objectives of one or more major subdivisions or ATO and NAS operations and may impact on the
objectives of the FAA. In some areas, the work may affect the safety and security of customers.


Builds, maintains, and uses the tools and capabilities needed to measure, monitor, analyze, and optimize National Airspace System (NAS) maintenance operations. Identifies methods, processes, algorithms, tools, and systems to extract and interpret findings from varied structured and unstructured data sets related to the data science life cycle. Applies complex and highly specialized data sets to support and improve Technical Operations maintenance operations in terms of operational efficiency, cost efficiency, and safety. Uses a combination of computational and machine learning methods to develop machine learning models; and advanced programming languages such as R or Python, to evaluates model performance and selection and extracts data without preset specifications. Develops
computational algorithms and statistical methods to find patterns and relationships in large volumes of data. Develops machine learning models utilizing machine learning (ML) algorithms, ML model development, and evaluation, such as supervised and unsupervised learning, to develop machine learning models.

Assignments frequently require knowledge and experience working across functional and/or organizational lines. Acts as principal technical specialist working with teams to derive information from data to solve previously unsolvable problems. Collaborates with engineers and data and analytics teams from across the Air Traffic organization to aid in the development of data analysis capabilities to help with decision-making and maintenance optimization. Works with operations and safety groups within the ATO to identify and analyze opportunities to improve safety, operational efficiency, and cost efficiency. Coordinates with subject matter experts within the
FAA and other Lines of Business on various issues related to the analysis of data. Performs statistical modeling, such as regression, classification, and anomaly detection to analyze various datasets from the aviation industry, including NAS equipment telemetry data, maintenance records, supply support records, operational performance data, and aviation support industry data.

The Data Scientist organizes and interprets data to inform decision-makers. Leads risk identification efforts and mitigation assessments. Possesses knowledge of the aviation industry, and public and private sector entities that operate within the NAS, analyzes occurrence and event data, electronically gathered data, and voluntarily reported information. Identifies risks and opportunities in the NAS through the use of data sets.


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