Waters Global Research is looking to enhance customer user experience by building more intelligent analytical chemistry instruments. This involves developing systems that can self-diagnose and self-heal, automating manual data interpretation and optimization processes, and training machine learning models to perform root error diagnosis using raw signal time series data.
Requirements
- Strong skillset in Python programming language, Numpy, Pandas and Scipy libraries
- Ability to comfortably and naturally write code in an object-oriented way
- Knowledge / experience with machine learning models
- Knowledge / experience reading online references and research literature and applying algorithms to the code
- Comfortable with Git version control, BASH shell, and virtual environments
- Knowledge/experience with AWS Lambda, S3 and Sagemaker
- Experience designing high performance algorithms
Responsibilities
- Support the staff ML Engineers to build out new techniques to improve the models
- Create baseline models, and build/improve the test harness to properly compare alternatives
- Use object-oriented programming and functional programming best practices to maintain codebases and write unit tests
- Contribute to the construction and maintenance of the AWS data pipeline
Other
- The role of this coop is to aid the team to collect and augment specialty instrument data, develop time series classification and prediction models, and experiment with new data features as well as new algorithmic features to maximize the efficiency and efficacy of the results.
- We are looking for someone with a growth mindset who is self-motivated, and ready to overcome obstacles to develop a working research prototype.
