Machine Learning Engineer - Retail Banking and Wealth Management
Central, Hong Kong Island, Hong Kong

The well-being of our employees and candidates is really important to us, that's why we are leveraging our digital capabilities to ensure we can continue to hire top talent at HSBC in the current environment.

Our teams will talk you through how our Video Interviewing technology will replace in person interviews and be used throughout the recruitment process.

Our team will be on hand to guide you through this process.

Some careers have more impact than others .

If you're looking for a career where you can make a real impression, join HSBC and discover how valued you'll be. Whether you want a career that could take you to the top, or simply take you in an exciting new direction, HSBC offers opportunities, support and rewards that will take you further.

Retail Banking and Wealth Management serves millions of customers worldwide with a complete range of banking and wealth management services to enable them to manage their finances and protect and build their financial futures.

It is a global business that brings together management responsibility for Retail Banking, Wealth Management, Insurance and Asset Management with a focus on customer-centric propositions and innovative and efficient distribution channels.

The Data Science Innovation group, part of HSBC's retail bank digital group, aims to be an agile and start-up like incubation team.

We work from concepts to working prototypes that we can scale up to production in quick time.

The group is looking to bring to life products that leverage data and machine learning and bring a new and improved customer experience to our customers.

We are building products that include recommendation systems, chatbots, graph network analytics and knowledge graphs, forecasting systems among others.

As a Machine Learning Engineer, you will mix the power of data and machine learning with software to bring new products into the market.

You will leverage your experience on getting Machine Learning models into production and into apps to work with a full stack team and build the next generation of data products.

Principal responsibilities

  • Develop data products from inception to production ensuring sound software architecture decisions, coding practices and scalability issues
  • Develop and manage data pipelines and APIs
  • Develop ML models and design a production path including data quality issues and scalability issues
  • Requirements

  • Master's or Bachelor's degree in Computer Science, or a related technical field. Having a PhD with ML exposure is beneficial
  • Understanding about basic computer science concepts like Data Structures Strong Python skills along with some exposure to frameworks like Tensorflow, PyTorch etc
  • Knowledge about concurrency in Python including asynchronous programming
  • Exposure to some NoSQL databases like Elasticsearch, MongoDB, Neo4j
  • Demonstrable experience bringing ML into production Exposure to Linux and cloud technologies
  • Knowledge about containerization including Docker, Kubernetes etc
  • Candidate with less relevant experience or skills may be offered a lower Global Career Band than stated above.

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