About the Job
Gradiant is a global solutions provider and developer of cleantech water projects for advanced water and wastewater treatment. Gradiant’s end-to-end solutions and technology expertise enable sustainable and cost-effective treatment of the world’s most important water challenges.
With a full suite of differentiated and proprietary technologies, powered by the top minds in water, Gradiant serves its clients’ mission-critical operations in the world’s essential industries.
Gradiant was founded at the Massachusetts Institute of Technology (MIT) and is uniquely positioned to address the world’s increasing challenges created by industrialization, population growth, and water stress. The company has global headquarters in Boston, regional headquarters and global R&D innovation labs in Singapore, and offices across ten countries.
Recently Gradiant acquired Synauta, a Canadian software company which uses machine learning to optimize energy and chemical processes at water treatment facilities around the world. Synauta was also recently recognized as the Breakthrough Technology Company of the Year at the 2022 Global Water Awards. More info gradiant.com and synauta.com.
You will be using ML and ML Ops best practices in a team of machine learners, working closely with data scientists, application analysts and application engineers. Opportunities include building and improving existing cloud-based, cutting-edge ML platforms, pipelines and experiments. Refining and testing existing deployments and using big data tech to achieve customer goals.
Essential Duties & Responsibilities:
- Measuring, monitoring, optimizing and recommending changes to productionized models
- Managing infrastructure associated with machine learning and advanced analytics
- Transforming and converting data science prototypes
- Performing statistical analysis
- Running machine learning tests including existing deployment improvements and new deployments, and ensuring the broader team understands the limitations of each model
- Identifying differences in data distribution that influences model performance
- Working closely with the Principal ML Developer, Senior Data Scientist and Data Scientist to identify effective models to optimize various water treatment processes
- Practical experience with ML algorithms, techniques and packages
- Demonstrated capability in a developer role, ideally with analytical experience
- Experience with the data science ecosystem including Python and Jupyter Notebooks
- Hands on experience with Kubernetes
- Experience in building and running Docker images
- 2-3 years of work experience in a business environment
- University or college education in Computer Science, Engineering or Advanced Analytics
- Cloud technology and/or Big Data certification.
- Experience with third party frameworks or tools and recommendations on tools to solve existing challenges