Born from Hong Kong's demand for fast, convenient, and reliable payment solutions, Octopus introduced the world's first contactless multi-modal transit payment system in 1997. Since then, this homegrown FinTech company has pioneered innovative payment solutions for urban living across four continents. Our Vision To become the most preferred payment and lifestyle companion that connects customers and business partners through our best-in-class products and services. Our Mission Making everyday life easier. Our Values Customer Centricity, Simplicity & Trustworthiness. Dedicated to addressing customer needs and adapting to evolving market trends, Octopus has broadened its services beyond transportation to encompass retail, e-commerce, cross-border transactions, and travel abroad. Today, we serve approximately 98% of Hong Kong’s population, processing around 15 million transactions at more than HK$300 million on average daily. At the heart of our success are our colleagues. We value mutual respect, foster collaboration, and encourage innovation and partnership. Join us and shape the future of payment solutions. Your impact starts here!
Responsibilities:
- Discover insights for our business through developing management dashboards, regular reports, and ad-hoc analysis.
- Prepare presentations and translate statistical results into business recommendations.
- Solve business problems or revamp existing processes using data science skills (machine learning, statistics, mathematical optimization, business intelligence, automation).
- Develop statistical models in customer segmentation and retention.
- Implement Statistical Analysis, Machine Learning Models & Data Pipeline on Azure Databricks, PySpark, SQL.
- Collect user requirements and manage internal projects with business & IT teams.
- Collect unstructured and structured data and integrate with our internal data-source.
Requirements:
- University Degree in Data Science, Information Technology, Engineering, Statistics, Business, or related disciplines (Master Degree is preferred).
- Around 6 - 8 years' experience in designing and implementing data-driven solutions. Expert in Python and SQL. Relevant experience gained from Finance or Retail industry is a plus.
- Experience in Databricks, Cloud, or GenAI is highly preferred.
- Strong understanding of statistical modelling and machine learning.
- Excellent analytical skills – ability to identify trends, patterns and insights from data. Good presentation skills with ability to communicate technical details to non-technical audiences.
- Work independently, proactive with a can-do mindset; think strategically and open to tackling new issues.
- Good written and spoken English and Chinese.
We offer successful candidate an attractive remuneration package and excellent career prospects. Interested parties please send your resume, present and expected salary, contact details and quoting the reference number by clicking "Apply Now"
Visit our web site: http://www.octopus.com.hk/
The personal data collected will be used for recruitment purposes only. If you are not contacted by us within six weeks, you may consider your application unsuccessful. Personal data with an unsuccessful applicant will be destroyed 12 months after rejection of the application. During this retention period, you have the right to request for correction or destruction of your personal data at any time. Any request for the correction or destruction of personal data should be addressed in writing to our Human Resources & Administration Department.
Octopus is an equal opportunity employer and all employment decisions and Human Resources policies are administered; especially those relating to recruitment & selection, compensation & benefits, promotion & transfer, training & development and termination & redundancy; without discrimination on the basis of age, race, colour, religion, sex, national origin, marital status, pregnancy, physical and mental disability and family status but on genuine occupational qualification, job performance, employees’ ability and internal/ external relativities.