VP, Team Lead, Machine Learning Engineer (AI Industrialization), Group Consumer Banking and Big Data Analytics Technology, Technology & Operations
Business Function Group Technology and Operations (T&O) enables and empowers the bank with an efficient, nimble and resilient infrastructure through a strategic focus on productivity, quality & control, technology, people capability and innovation. In Group T&O, we manage the majority of the Bank's operational processes and inspire to delight our business partners through our multiple banking delivery channels. Job Purpose
Build and improve machine learning and analytics platform. Work with data scientists to create, optimize and productionize of machine learning models for various business units within the organization. Keep innovating and optimizing data and machine learning workflow to enable data-driven business activities at large scale. Responsibilities
- Build and improve machine learning and analytics platform.
- Apply cutting edge technologies and tool chain in big data and machine learning to build machine learning and analytics platform.
- Keep innovating and optimizing the machine learning workflow, from data exploration, model experimentation/prototyping to production.
- Provide engineering solution and framework to support machine learning and data-driven business activities at large scale.
- Perform R&D on new technologies and solutions to improve accessibility, scalability, efficiency and us abilities of machine learning and analytics platform.
- Work with data scientists to build end-to-end machine learning and analytics solution to solve business challenges.
- Turn advanced machine learning models created by data scientists into end-to-end production grade system.
- Build analytics platform components to support data collection, exploratory, and integration from various sources being data API, RDBMS, or big data platform.
- Optimize efficiency of machine learning algorithm by applying state-of-the-art technologies, i.e. distributed computing, concurrent programming, or GPU parallel computing.
- Establish, apply and maintain best practices and principles of machine learning engineering.
- Study and evaluate the state of the art technologies, tools, and frameworks of machine learning engineering.
- Contribute in creation of blueprint and reference architecture for various machine learning use cases.
- Support the organization in transformation towards a data driven business culture.
- Work closely with data scientists, business team, and project managers to provide machine learning and data-driven business solution.
- Collaborate with other technology teams to build platform and framework to enable machine learning and data analytics activities at large scale
- Maintain engineering principles and best practices of machine learning framework and technologies.
Apply Now We offer a competitive salary and benefits package and the professional advantages of a dynamic environment that supports your development and recognises your achievements.
- PhD/Masters/Bachelors in Computer Science, Computer Engineering, Statistics, Applied Mathematics, or related disciplines.
- 5+ years of experience in software engineering or DevOps automation or data engineering
- Excellent understanding of software engineering principles and design patterns.
- Excellent programming skills in either Python, Scala, or Java.
- Hands-on experience in containerization/ virtualization platforms, e.g. Docker/Kubernetes.
- Exposure to data science and machine learning technologies and methodologies.
- Good working knowledge of high performance computing, parallel data processing, and big data stack, e.g. Spark and Hadoop/Yarn.
- Experience to one or more commercial / open source data warehouses or data analytics systems, e.g. Teradata, is a big plus
- Experience to one or more NoSQL databases is a big plus.
- Experience or Cloudera Data Science Workbench, is a big plus.
- Passion about machine learning and data-driven intelligence system.
- Excellent communication and presentation skills in English.
- Team player, self-starter, ability to work on multiple projects in parallel is necessary.
- Experience working in multi-cultural environments