Aviation Programmer Analyst
The Position
ATAC has an opening for an Aviation Programmer Analyst. This is a full-time mid-level staff position and located in the Washington, DC, area. The successful candidate will participate in the development of data processing and reporting tools supporting the analysis of aviation data at the Federal Aviation Administration (FAA).
Responsibilities include:
Designing and building ML-driven analytical tools that accept user-specified metrics, threshold criteria, and historical time periods, then identify clusters of anomalous NAS performance and explain their root causes
Performing regression analysis and feature engineering on large, multi-source aviation datasets
Working with FAA stakeholders to develop Python-based capabilities that ingest air traffic performance metrics, apply clustering, regression, and other ML techniques to enable causal analyses and produce explainable outputs that quantify the contribution of each causal factor.
Using Python, Java, R and/or other languages and tools to create extract, transfer, load (ETL) processes generate metrics, find anomalies, and determine causal factors in aviation data
Implementing model-explainability techniques (e.g., SHAP) so that analytical results are transparent to FAA decision-makers
Supporting FAA activities starting at 06:00 Eastern Time
Supporting other analysts/developers in the deployment of dashboards into a production environment
Writing documentation for code/processes using Confluence
A background check will be required for this position.
Qualifications
ATAC is looking for career-minded people with the potential to grow professionally and advance into positions of greater technical and project management responsibility.
Required attributes include:
BS degree in Mathematics, Computer Science, Data Science, Engineering, or related fields, and 3 or more years of relevant experience
Strong proficiency in Python for data analysis, statistical modeling, and machine learning (NumPy, pandas, scikit-learn, or equivalent)
Experience with the Java, R, and JavaScript programming languages
Experience with deploying ML models into a production environment and determining features from large datasets
Experience using Oracle and PostgreSQL databases to retrieve, process, and store data
Experience working in Windows and Unix/Linux environments
Experience using Git and other collaborative tools
Excellent skills with Microsoft Office including Excel, PowerPoint, and Word
Excellent written and oral communication skills and interpersonal relations skills
Desirable attributes include:
Masters degree in Mathematics, Computer Science, Engineering, or related fields and 1 or more years of relevant experience
Air traffic data analytical expertise (especially with air traffic trajectory data, NAS performance metrics including OPSNET, TBFM, NTML, OOOI, and other large data sets)
Experience with data visualization libraries (Matplotlib, Seaborn, Plotly) for communicating analytical results
Experience with ML/statistical techniques such as ensemble methods (random forests, gradient boosting), time-series analysis, and anomaly/outlier detection
Experience with geographical information systems and/or geo-computational methods
Experience building Tableau and Leaflet dashboards
A strong interest in the aviation field and solid understanding of the National Airspace System and FAA objectives
In addition, the ideal candidate is a team player, open to new ideas, technologies and development methodologies, thinks both logically and creatively, and approaches problems and problem solving with a positive and constructive attitude.
ATAC offers a casual business environment within a supportive team of innovative individuals. Pride in ATAC’s working and aviation-focused environment and culture is key to our success. Those who share this passion will thrive here. If this sounds like you, please send your resume, including a cover letter in which you summarize the particular elements of your background that relate to the requirements of this position.
ATAC is EOE, Disabled/Veterans.
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