What are the responsibilities and job description for the Internship Opportunity: Data Science and Research & Innovation Support Intern position at ASSOCIATION OF AMERICAN RAILROADS?
Project Name: Data Science/Research & Innovation Support
Interns Preferred Degree: Currently pursuing a master’s or Ph.D. in engineering, computer science, or other relevant degrees.
Project Description:
The projects’ general objectives include supporting research by way of data collection to improve railway safety, performance, and efficiency. Projects supported may include, but are not limited to, Exploratory Data Analysis (EDA), Supervised Machine Learning (ML) Applications, Feature Importance, Rail Inspection, and Wheel & Axle Inspection Technology. These projects may be funded by the Association of American Railroads as part of MxV Rail’s Strategic Research Initiatives program, through the Federal Railway Administration (FRA), MxV Rail’s internal research and development, and/or commercial customers.
Primary Duties:
Conduct data analysis, including, but not limited to:
- Exploratory Data Analysis (EDA).
- Supervised machine learning model development and deployment.
- Feature importance and interpretability analysis.
- Statistical analysis of track, wheel, axle, and mechanical data.
- Track and mechanical data collected at FAST or other sources.
- Model development: Assist in building, training, and fine-tuning machine learning and deep learning models, with a primary focus on time series data (e.g., raw UT waveforms, electromagnetic data, other modalities).
- Data processing: Participate in the collection, cleaning, and preprocessing of large datasets used for training and evaluating AI models.
- Algorithm research: Conduct research on state-of-the-art algorithms and emerging AI technologies to improve the accuracy and efficiency of inspection systems.
- Prototyping and testing: Develop proof-of-concept models and test them against real-world data to evaluate performance. Work with engineers to test and validate deployed models.
- Code generation: Write clean, well-documented code in Python to support data pipelines, model building, and system integration.
- Data Fusion: Explore data fusion approaches for UT and electromagnetic data.
- Collaboration: Work closely with cross-functional teams to integrate AI/ML solutions into research.
- Reporting: Document findings, methodologies, and results, and present progress to technical and non-technical stakeholders.
- Ensures that all duties and responsibilities are performed in a safe manner.
- Perform other related duties as assigned.