Data Scientist

Specific responsibilities will include:

• Contribute to the delivery of Credit Risk advisory engagements across Singapore, Malaysia and Thailand.
• Design and develop data driven exploratory analysis as well as predictive and decision models to solve business problems across the financial service industry.
• Build and evaluate predictive scoring models and data driven strategies using advanced statistical modelling techniques. This includes the analysis of large amount of VisaNet payment transaction data, bank’s internal data, bureau data, alternative data, etc. Determine the suitability for modelling, data cleansing, trend analysis, features engineering, sampling, performance definition analysis, variable shortlisting, statistical model development experiments with different types of modelling algorithm and techniques, model validation and strategy development.
• Ensure all project documentation is up to date and maintain high level of quality in deliverables.
• Present models and analytical results to technical and non-technical audience.
• Collaborate with internal and external clients throughout the end-to-end delivery. Set priorities and manage projects of small to medium scale, identify roadblocks and work to resolve them.
• Proactively communicate project status and deliverables. End-to-end delivery of multiple projects from concept to implementation within timeline, scope and budget requirements and in line with client expectations.
• Drive high standard of customer consulting service and proactive client communication throughout the project.
• Explore leading methodologies and best practices with the wider VCA teams and import successful methodologies from other global markets.
• Flexible to travel to client locations for project delivery (30%).
• Participate in business development and the expansion of VCA risk services.

This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.

Requirements

Knowledge and Experience
• Proficiency in modelling tools, strong programming skills in Python and SQL. Additional experience in R and/or SAS would be beneficial.
• Practical experience in predictive modelling and risk strategy applications such as Application / Behavior / Collection scoring. Understanding in PD, EAD, LGD and IFRS9 models is a plus.
• Proficiency in the use and application of analytical methodologies including Linear Regression, Logistic Regression, CHAID, CART and other advanced Machine Learning algorithms (classification: Gradient Boost, Random Forest, Deep Learning, etc).
• Good understanding of banking data and ability to investigate and analyze database and data processing problems.
• Proven skills in translating analytical outputs to actionable recommendations. Experience with facilitating and giving presentations to various audience levels.
• Self-motivated, results oriented individual with the ability to handle multiple projects concurrently.
• Results oriented with strong analytical and problem-solving skills, with demonstrated intellectual and analytical rigor.
• Detailed oriented, the candidate is expected to ensure highest level of quality/rigor in deliverables, reports and data analysis.

Qualifications
• Bachelor’s degree in Computer Science, Statistics, Engineering, Mathematics or related quantitative field.
• Candidate with 6+ years’ of credit risk analytics experience across customer life cycle.