About our client
My client is one of the biggest conglomerates in the Philippines, and the have there headquartered based in Singapore. They have embarked on a journey to democratize financial services and bring next-level banking to the ASEAN region. They are seeking individuals who share their belief that the game is worth changing, to join our growing team as they are building and launching and scale a bank that empowers all.
We’re looking for a passionate and energetic Data Scientist focusing on Credit decisioning and Lending analytics. The ideal candidate will have to apply themselves to identify and make use of all our fit for purpose internal and external data sources, whether they’d be structured, unstructured, semi-structured, traditional data sources and /or alternative data sources, whether in batch or streaming (real time or near real time), and whether they are collected via the front end, middleware and / or backend to develop the best in class lending and credit management capability for our business.
The aim is to drive and implement actionable insights across the full end to end customer journey and various interactions and touch points from origination and underwriting, active base management, collections and recoveries up to settlement and repeat sales with a view to optimize the lending portfolio and manage the inherent credit risk amongst others.
Assist in the development of the credit scorecards and models.
Enhance the existing analytical techniques and capability by promoting new methods and best practises emerging from the data science and machine learning fraternities.
Proactively seek out opportunities to innovate by using non-traditional and alternative data and new modelling algorithms and techniques to optimize returns on the lending portfolios by managing the inherent risk and performance stresses.
Work closely with the Head of Credit Risk and /or the Head of Lending.
Ensuring all development and implementation documentation is up to date and maintains the highest levels of quality for traceability and auditability.
Ensure the relevant tracking and reporting frameworks and dashboards are setup to give an overview on the onboarding flow from attempts to apply, approvals and declines, high side / low side referral.
You will have
Bachelor’s degree or higher with any field with advanced quantitative focus, including but not limited to Psychometrics, Statistics, Applied Mathematics, Physics, Chemistry, Biology, Econometrics, Engineering, etc.
5+ years of data analytics experience and applying statistical solutions, including 3+ years of hands-on experience analysing data in a credit risk and lending environment.
Experience applying data science techniques within a commercial environment, ideally in a credit risk environment, with proficiency in most of the following, Linear & Logistic Regression, Decision Trees, Random Forests, Markov Chains, Support Vector Machines, Neural Networks, Clustering, Principal Component Analysis, Factor analysis, Boosting algorithms etc.
Ability to exploit insights from big data sets (Working with data size from Terabyte to Petabyte scale) and write the necessary scripts to extract these.
Firm knowledge around statistical methods, model development and experimental design with a deep understanding of modern machine learning techniques and mathematical underpinning.
Excellent judgment and problem-solving skills.
Good experience with Python, PySpark, R, Java and/or Scala, SQL is preferred (3+ years) with solid project experience.
A self-starting mindset along with strong communication and collaboration skills.