Machine Learning Engineer - Hà Nội - TA139Ngân hàng TMCP Việt Nam Thịnh Vượng - VPBank

Mô tả Công việc
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
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.
Conduct research on modern methods for machine learning and engineering.
Proactively analyze and utilize existing/new data sources to support more impactful analyses
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