Brooks Automation was recently named 1 of the fastest growing companies of 2021 by Fortune Magazine, and we'd love for you to be part of that growth!
Lead Machine Learning and predictive algorithm development, while engaging in research, design and automation implementations, delivering resolutions that have a direct business impact which are tailored to specific robot product line.
Exploratory data analysis, Model creation, model deployment and optimization
Develop tools to mine, clean, and analyze raw datasets detecting potential trends and insights
Work with datasets on data association, clustering, segmentation, and filtering
Apply feature engineering and statistical methods on datasets
Determine suitable machine learning infer algorithm to use, for best application implementation of a predictive analytic tool and troubleshooting guide.
Initiate and collaborate with engineering teams to build model design, optimizing processes, and automating configuration and testing
Sample of expected Machine Learning implementation
For each automation suite implementation, an automation raw data gathering module is developed, which become a part of a data processing component sequence (data pipeline), data filtration module, visualization to gain insight, extracting / calculating attributes (features) and looking for correlations, reducing dimensions (PCA), scaling methods, moving into transformation pipelines and be used to fit = train computer models.
These models classify assemblies to be deemed suitable to move on the production line or not (providing the technician with possible HW causes for the failure and suggested of action to take).
These ML implementations provide ability to capture defects on the production line where assemblies are built, hence troubleshooting issues down the line reduced, and assemblies can be corrected on the spot. Quality and first pass yield increase, and product delivery time is as promised to our customers.
B.S. degree in Engineering, Computer Science, Mathematics, Statistics, Economics or other related fields.
+2 years’ experience as Data Scientist (engineering / manufacturing environment is desirable)
Programing languages: 2+ years of hands-on coding experience with Python (PyCharm and JupyterLab)
Machine Learning associated modules: Experience with Scikit-learn, Pandas, NumPy, Keras, TensorFlow, OpenCV, Plotly
Solid knowledge and experience in the following fields: Feature Engineering (it’s all about the data), Machine Learning techniques / algorithms specifically in unstructured data, and data visualization for the manufacturing environment.
Comfortable working with and around both Mechanical and Electrical equipment.