Time Series Machine Learning Intern

What will you do?

• Develop Python-based time series machine learning algorithms for fleetwide IoT data, including anomaly detection and classification.
• Create frameworks for prototyping and benchmarking machine learning algorithms and tools on data across fleets of similar industrial assets.
• Select appropriate datasets and data representation methods.
• Run machine learning tests and experiments to support the product development.
• Develop and test strategies for training and retraining machine learning systems.
• Research and survey industrial reports and technical papers on latest industrial trends, case studies, models and methodologies.

What’s in it for you?

• The expected compensation range is $22.50 – $39.50 per hour.
• This position is eligible for overtime pay and recognition programs.
• The compensation rate for this position is for candidates located within the United States.
• Individual pay is determined by several factors including knowledge, job-related skills, experience, and relevant education or training.

Qualifications

• The candidate must be currently enrolled in a masters or doctoral graduate program in a related discipline such as Mathematics, Statistics, Physics, Engineering, Data Science or CSDemonstrable experience in full stack data science, including developing and deploying machine learning models in a production environment.
• Experience working with time series data is required.
• Prior experience with Python-based libraries for time series, such as SKTime, tsfresh, Ruptures will be an advantage.
• Familiarity with time series machine learning algorithms/ frameworks like ROCKET, Matrix profile techniques, contrastive learning, and architectures like InceptionTime and other 1D Convolutional Neural Networks will be a plus!
• Familiarity with time series foundational models, like MOMENT, TimeGPT and Granite will be a plus!
• Experience exploring and training models with big datasets, using cloud technologies (especially Databricks).
• Proficient in scientific Python (NumPy, SciPy, scikit-learn, Pandas, XGBoost), SQL, and version control (GitHub).
• Ability to formulate hypotheses, draw conclusions and deliver results.
• Excellent verbal and written communication and interpersonal skills.
• Ability to present findings to stakeholders and communicate results to all colleagues.