Data Scientist

Application Deadline: 25 August 2025

Department: Technology-CDSIO

Employment Type: Permanent - Full Time

Location: Hong Kong (SAR)

Description

About Mox
Mox is built by and for the ones who aspire to live life to the fullest – we call them Generation Mox! The name Mox reflects the endless opportunities we can create, - Mobile eXperience; Money eXperience; Money X (multiplier), eXponential growth, eXploration… it’s all up for us to define together.

Why Mox
Everything at Mox – from our products, features, to rewards – is designed based on customer research, tailor made for your needs. We care about what customers care about, especially in data security and privacy. Data ethics is core to everyone here at Mox. Mox rewards you with an array of banking and lifestyle benefits. Who says banking can’t be fun?

What we are looking for?

As a Data Scientist you'd be working with us as an enabler of quality data-driven decision making. Your work will involve uncovering insights from data, surfacing these insights with best-in-class visualizations to drive decision making, and then going on to implement advanced models that help maximise business value. You will be a champion for experimentation and testing to ensure that decisions made on product are backed up with statistical rigor.  You will also work with different functional teams across the bank and be a driving force in continuously levelling up our already data-driven culture.

Key Responsibilities

• Proficiency using Python and SQL for data analysis and modelling with large data sets
•  Experience using business intelligence platforms (eg. Tableau, PowerBI) to visualize, explore and share data
• Experience working with a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and understand their real-world advantages/drawbacks
• General AWS services (or other cloud platform equivalents):  Glue, Athena, S3
• Experience applying multivariate experimentation and hypothesis testing
• Exposure to data science environments (eg. AWS Sagemaker, Jupyter, MLFlow)
• Data Analysis/ML frameworks and tools: Pandas, Numpy, Sci-Kit Learn, Keras, Pytorch
• Agile/Lean project methodologies and rituals: Scrum, Kanban
• Version control: git commands, branching strategies, collaboration etiquette, documentation best practices
• Workflow scheduling and monitoring tools: Apache Airflow, Luigi, AWS Batch