About the Role :
We are looking for a data scientist who will work in Internal Audit area and familiar with a broad spectrum of modelling techniques (both classical ML and various deep learning modelling approaches) utilising a wide variety of data types (e.g. transactional financial data, system log data, clickstream data, geospatial data, customer support chat log, audio recordings, image and video data, etc) to be able to build models that support and providing assurance on:
- Digital banking product features such as smart financial recommendations around how much to save when
- Optimisation of growth and marketing functions such as dynamic content and marketing recommendations to customers
- Business operations optimisation
- Real-time fraud detection and other risk management functions
- Credit risk management
- Improving the efficiency of various technical operations with the business
What You Need to Have :
- A Bachelor’s, computer science, statistics, physics, mathematics or other related degree. Advanced postgraduate degree and ancillary degrees or courses in finance, economics, actuarial and related disciplines would be valued.
- Understands the theoretical foundations underpinning machine learning and deep learning models while also has hands-on experience dealing with the problems they throw up in the real world.
- 5+ years work experience
- 1+ years experience deploying machine learnings in production environments
- 3+ years building machine learning and deep learning models.
- Having core competencies and experiences of data scientist, i.e.: Statistical analysis, machine learning, computer science, numerical analysis, and software engineering. Programming: Write computer programs and analyze large datasets to uncover answers to complex problems. Comfortable writing code working in a variety of languages such as Java, R, Python, SQL and other computing tools.
- Experience with financial modelling of moderate complexity is desirable, but is not a requirement.
- Strong communication skills are essential to help communicate complex ideas to less technical audiences.