Senior Data Scientist
5 days ago
**About DKatalis**
DKatalis is a financial technology company with multiple offices in the APAC region. In our quest to build a better financial world, one of our key goals is to create an ecosystem linked financial services business.
DKatalis is built and backed by experienced and successful entrepreneurs, bankers, and investors in Singapore and Indonesia who have more than 30 years of financial domain experience and are from top-tier schools like Stanford, Cambridge London Business School, JNU with more than 30 years of building financial services/banking experience from Bank BTPN, Danamon, Citibank, McKinsey & Co, Northstar, Farallon Capital, and HSBC
**About the role**
We are looking for a data scientist 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:
- Digital banking product features such as smart financial recommendations around how much to save when
- Customer acquisition, engagement, segmentation, and lifetime value estimation
- Business operations optimisation
- Fraud detection and other risk management functions
- Improving the efficiency of various technical operations with the business
You will be expected to lead and contribute to the end-to-end delivery of data science and machine learning solutions from ideation to model operationalisation and maintenance independently and as part of a team. A machine learning engineer from the data science platform team will guide and support you when it comes to operationalising models within a production environment. Having engineering skills in areas relevant to data science is appreciated.
Strong communication skills are essential to help communicate complex ideas to less technical audiences.
Experience with financial modelling of moderate complexity is highly desirable as some of the features that will be built out will involve forecasting and support personal financial management product features.
**Requirements**:
- A MSc or Phd in computer science, statistics, physics, mathematics or other related degree. 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.
- 1+ years experience deploying machine learnings in production environments
- 3+ years building machine learning and deep learning models.
- Strong familiarity with tools within the PyData ecosystem such as Numpy, Scipy, pandas, scikit-learn, PyTorch, Tensorflow, spaCy, etc.
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