BCTAS

1.Working with other centers/departments in EDA as well as Business Units to understand business problems so as to support them in better utilizing machine learning.

• Managing and directing a team of machine learning engineers in applying data science and engineering techniques in packaging data analytic models developed by Data Science Office.

• Managing and directing a team of machine learning engineers in developing and operating the MLOps platform.

• Partner with data scientists to build and operate Feature stores.

• Collaborate with relevant units to pull new data sources for AIC to facilitate developing new use-case.

• Partner with data scientist to build criteria for validating machine learning model.

• Develop deployment architecture for machine learning model, leverage on premise and cloud based big data platforms to refactor and optimize code for production.

• Automate data and machine learning engineering processes.

• Monitor model performance, business impact after deployment; support retrain to improve model quality.

• Coach & train team members.

2. Conduct research and acquire new machine learning techniques

• Conduct research on modern methods for machine learning and engineering.

• Proactively analyze and utilize existing/new data sources to support more impactful analyses

3.Partner business units on advanced analytics-related problems

• Working with other centers/departments in EDA as well as Business Units to understand business problems so as to support them in better utilizing machine learning

• Training other EDA team members on machine learning