Data Scientist

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 common modeling techniques (both classical ML and basic deep learning approaches) and working with various data types (e.g., transactional financial data, system log data, clickstream data, customer support chat logs) to help build models that support:

• Digital banking product features such as financial recommendations
• Customer segmentation and engagement analysis
• Business operations optimization
• Assisting with fraud detection and risk management functions
• Supporting the improvement of various technical operations within the business
  
  

We value candidates who are self-motivated, proactive problem-solvers with a can-do attitude. The ideal candidate demonstrates persistence and grit when facing challenges and has a track record of initiating and completing projects.

You will contribute to the delivery of data science and machine learning solutions from ideation to model development as part of a team. You'll work closely with senior data scientists and machine learning engineers who will guide you in model development and operationalization. Developing engineering skills relevant to data science is encouraged and supported.

Familiarity with basic financial concepts is beneficial, as some projects will entail forecasting and/or supporting personal financial management product features, for instance.

Requirements:

Education and Background

• A Bachelor's or Master's degree in computer science, statistics, physics, mathematics, or a related field.
• Courses in finance, economics, or related disciplines are a plus.
  
  

Technical Skills

• Proficiency in Python and its data science ecosystem, including tools such as Numpy, pandas, scikit-learn.
• Basic knowledge of deep learning frameworks like PyTorch or TensorFlow.
• Experience with or exposure to version control systems (e.g., Git) and collaborative coding practices.
• Basic understanding of data visualization techniques and tools (e.g., Matplotlib, Seaborn, or Tableau).
  
  

Knowledge and Experience

• Understanding of fundamental statistics and machine learning concepts, with a willingness to learn and apply them to real-world problems.
• 1-3 years of experience working with machine learning models, including some exposure to production environments.
• Ability to analyze and understand how to use data to solve pertinent business problems.
  
  

Soft Skills and Attributes

• Strong analytical and problem-solving skills.
• Excellent communication skills, both written and verbal.
• Ability to work collaboratively in a team environment.
• Eagerness to learn and grow in the field of data science and financial technology.
  
  

Preferred Qualifications:

• Familiarity with or interest in the financial technology or retail banking sector.
• Contributions to or involvement with data science communities or clubs at university or online.
• Demonstrated ability to learn and adapt to new technologies quickly.
• Exposure to big data technologies or cloud computing platforms (e.g., AWS, GCP, or Azure).
  
  

Stand-out Qualities

• Experience in fintech or related industries (not necessarily banking)
• Strong portfolio of relevant projects (e.g., well-maintained GitHub repositories).
• Active participation in the data science community (giving talks, attending meetups).
• Demonstrated thought leadership (blog posts, articles, or contributions to open-source projects).
• Unique skills that complement the existing Data Science team.
• Basic understanding of cloud technologies and containerization (e.g., Docker, Kubernetes).