Data Science Intern

KBR, Inc.•Chicago, IL

2d•$53,800 - $80,700•Onsite

About The Position

Intern positions are available at NASA Ames Research Center to participate in the development of an risk assessment framework using aviation safety data. Time commitment is negotiable. Master’s through early PhD. The opportunity is focused on developing data-mining and artificial intelligence (AI) techniques to extract and analyze safety-critical information from aviation accident and incident reports. The project will leverage existing machine learning tools to process reports from the Aviation Safety Reporting System (ASRS) and National Transportation Safety Board (NTSB) databases and identify safety events, contributing factors, causal relationships, and recurring patterns. The intern will support the development of an automated data-mining pipeline for extracting and organizing this information and will analyze the resulting data to identify factor combinations, event frequencies, and trends across aviation safety reports. The extracted information will then be translated into quantitative risk inputs, including event likelihoods and conditional probabilities, to support the development of data-driven probabilistic risk assessment (PRA) models. The resulting methods and tools are intended to improve the use of historical aviation safety data for identifying emerging risks and supporting quantitative aviation safety assessment and decision-making.

Requirements

Strong Python programming language skills
Background in Bayesian networks, graph structure, probability and statistics.

Nice To Haves

Experience with large datasets
Interest in researching and developing methods for data analysis, fault mode and effect analysis (FMEA).
Good software engineering skills (design of unit tests, performance profiling and optimization, documentation, etc)

Responsibilities

Develop data-mining and artificial intelligence (AI) techniques to extract and analyze safety-critical information from aviation accident and incident reports.
Process reports from the Aviation Safety Reporting System (ASRS) and National Transportation Safety Board (NTSB) databases.
Identify safety events, contributing factors, causal relationships, and recurring patterns.
Support the development of an automated data-mining pipeline for extracting and organizing information.
Analyze resulting data to identify factor combinations, event frequencies, and trends across aviation safety reports.
Translate extracted information into quantitative risk inputs, including event likelihoods and conditional probabilities, to support the development of data-driven probabilistic risk assessment (PRA) models.