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The Aviation Safety & Analysis department is individuals with interest in applying their data science skills to deep and challenging problems in the aviation domain. We have a treasure trove of data including time series, text and sensor data and challenging problems to solve. We are seeking someone with aviation experience and skills in machine learning, deep learning, text mining, big data and software/application development.
If Sklearn, pyTorch, Keras,TensorFlow, LSTM, LDA, anomaly detection, entity extraction, and summarization mean something to you and you’ve applied them to aviation data, we want to talk to you. You are not required to have skills in all of these but should have hands-on skill in at least one area and the ability to dive deep in the data, and learn the necessary skills to get the job done.
The candidate will work with the FAA and international customers, leveraging one of the largest repositories of aviation data to improve the capabilities, safety, and efficiency of the aviation system. The candidate will be responsible for working both individually and with a team of data scientists in the following tasks:
Performing anomaly detection and capability development using a variety of aviation safety data
Leveraging AI/ML techniques and solutions to identify complex patterns for predicting safety hazards
Increasing the efficiency and quality of the vulnerability discovery process by tightly integrating automated processes, AI/ML techniques and human SME/analyst expertise
Develop and prototype AI algorithms and software tools.
Enhance and maintain current analysis tools, including automation of current processes using AI/ML algorithms.
Conduct quantitative data analysis using a variety of datasets, including developing retrieval, processing, fusion, analysis, and visualization of various datasets
Develop techniques that make use of both digital flight data, text-based safety reporting, and a variety of other aviation data sets both in a silo and in a fused environment to identify potential safety vulnerabilities
Demonstrable aerospace/aviation knowledge or experience, this is a must
Bachelor’s Degree in Aerospace Engineering, Computer Science, Mathematics, Statistics, Physics, Electrical Engineering, Computer Engineering or related fields
Hands-on Software Development Skills (Python-Preferred)
Experience or educational courses/projects in Machine Learning, and/or Text Mining Algorithms.
Ability to work closely with domain experts to develop tools/algorithms needed to answer research questions
Excellent Communication Skills (with the ability to explain tools and ML algorithms to a non-technical audience)
Proven ability to work independently to learn new technologies, techniques, processes, languages, platforms, systems
Strong analytic, inferencing, critical thinking, and creative problem-solving skills
5 years of relevant experience
Experience with Deep Learning Frameworks such as Keras, Tensorflow, PyTorch, Mxnet, etc. Ability to apply these frameworks to real problems in the ‘time -series’ domain
Experience with interpretability of deep learning models
Big Data Skills (Azure, Hadoop, Spark, recent deep learning platforms)
Visualizations/Web Development Skills (e.g. Tableau, D3, etc).
Practical experience with statistical analysis
Experience with text mining tools and techniques including in areas of summarization, search (e.g. ELK Stack), entity extraction, training set generation (e.g. Snorkel) and anomaly detection
Expert software development skills lifecycle including developing and maintaining good production quality code
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