Norfolk Southern is looking to leverage AI and Data Science to solve business problems by designing Deep Learning and Computer Vision algorithms to detect objects, issues, and defects from high-frame camera data of trains and rail cars. They also aim to create predictive models using real-time sensor data from locomotives to anticipate component failures.
Requirements
- Scripting and programing experience with Python and knowledge of Spark.
- Hands-on and theoretical knowledge of various machine learning and deep learning algorithm and frameworks such as: xgboost/LightGBM, Random Forests, SVMs, PCA, t-sne, kmeans, DBSCAN.
- Python, Spark, PySpark
- Tensorflow, Keras, PyTorch, and MXNet for Deep Learning, and OpenCV for traditional Computer Vision.
- Knowledge of the cloud computing environment (e.g. Databricks) is a plus
- Expertise with Time Series problems.
Responsibilities
- Effectively utilize appropriate statistical, machine learning, and deep learning techniques to solve various business problems.
- Collaborate with various departments to identify opportunities for process improvement and developing analytics use-cases.
- Evaluate accuracy and quality of data sources, as well as the designed models.
- Stays up to date with the latest models and changes in the technology.
- Design and develop (almost) production ready code.
- Communicate results to colleagues and business partners.
- Coordinate with application development teams to integrate developed models with existing applications.
Other
- Provide guidance, support and mentoring to junior team members.
- Bachelors in computer science, electrical engineering, machine learning, statistics, or related field.
- 1-3 years of experience as a Data Scientist, Research Scientist, Machine Learning Engineer or Operations Research.
- Hybrid 3 days on-site and remote work 2 days per week.
- Familiarity with the railroad industry is highly preferred.
