Data Science Manager

About the Company

Founded in 2015, Validus Capital Singapore is the leading SME financing platform in Singapore and is a wholly-owned subsidiary of GXS Bank, Singapore’s homegrown digital bank.

By leveraging cutting-edge technologies such as AI, data analytics, and supply chain financing, Validus Capital Singapore is committed to addressing the financing needs of small and medium-sized enterprises (SMEs) by providing fast, efficient, and accessible financial solutions that expand credit access and support business growth across the region.

In recognition of its innovation and contributions to SME lending, Validus Capital has received numerous accolades, including 1st place at the MAS Fintech Awards, 1st runner-up at the SFF Fintech Awards, and recognition as one of LinkedIn’s Top 15 Startups in Singapore, as well as several other industry awards.

Key Responsibilities

1. Develop effective business intelligence strategies and analytics solutions to drive growth and manage risk.
2. Apply supervised and unsupervised modelling techniques using conventional and alternate data to build predictive machine learning solutions in the areas of credit risk underwriting, fraud detection, customer analytics, and cashflow projection.
3. Develop and maintain the credit underwriting algorithm using statistical/machine learning techniques.
4. Collaborate with the team to maintain existing and build new Power BI dashboards that track Validus’ key performance metrics.
5. Support data engineers, product managers, and developers to develop and/or maintain the data pipelines for the Data Lake.
6. Interface with technology, product, credit, and other business teams to formulate solutions and drive product/process changes.
7. Communicate results and business impacts of insight initiatives to stakeholders in various business units.
8. Keep up with the latest advancements in machine learning and artificial intelligence, actively bringing innovative techniques and models into the organization’s data science initiatives.

Requirements

1. Bachelor’s or Master’s degree (preferred) in Computer Science, Data Science, Statistics, Mathematics, Economics, Finance, or a related field.
2. Hands-on experience in developing and implementing credit scorecards, credit limit estimation, fraud detection models, and customer analytics solutions. Experience in credit scorecard development using statistical/machine learning techniques is a strong plus.
3. Strong background in supervised and unsupervised machine learning, statistical modeling, and data science techniques.
4. Proficiency in Python, SQL, and data manipulation frameworks (e.g., Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Prophet).
5. Experience working with large datasets, data pipelines, ETL processes, and cloud-based data architectures (AWS Redshift, S3, Databricks, or similar platforms).
6. Familiarity with MLOps practices, IDE, Git, and cloud-based ML deployment (e.g., AWS SageMaker) is a plus.
7. Strong problem-solving skills and experience in applying data-driven insights to business problems.
8. Excellent communication skills with an ability to translate technical concepts for non-technical stakeholders.