About the Role:
To lead a cross-disciplinary data science team to build from scratch:
- User behaviour models
- User Lifetime Value (LTV) prediction systems
- Partner credit scoring systems
- Anomaly detection and risk identification systems
- Recommendation engines and user preference algorithms
To do more than “analyse” — to enable the system to understand users, predict trends, manage risks, and unlock commercial potential in the B2B domain.
Location: Town
Responsibilities
Strategy & Leadership
- Develop a company-wide data science roadmap, defining the role and deliverables of the "data platform"
- Build and lead a data science team (model development, data products, engineering support)
- Drive productization of data capabilities to serve key departments such as product, operations, marketing, and risk management
Modelling & Algorithms
- Design and deploy core predictive models including user LTV, risk assessment, and behaviour tagging
- Participate in the design of anomaly detection, risk identification, and key account recognition algorithms
- Establish A/B testing frameworks to evaluate model contributions to business growth
Data-Driven Product Design
- Collaborate with product managers and developers to convert model outputs into features or recommendation systems
- Deliver partner value modelling, personalized policy pricing, and strategic tools for the B2B segment
- Foster a “data-driven operations” culture to shift from experience-based to evidence-based decision-making
Required
- Master's degree or higher in Mathematics, Statistics, Computer Science, Economics, or related fields
- Minimum 5 years of practical experience in data science or machine learning, including 2+ years in team leadership
- Proficient in modelling techniques (clustering, regression, time series, Bayesian, graph models, etc.)
- Able to independently design various models such as user profiling, lifecycle, recommendation, and risk control
- Skilled in Python / R / SQL and familiar with modern data infrastructure (e.g., Airflow, Spark, Kafka)
- Proven track record of deploying models that drive tangible business results (ROI, retention, conversion, etc.)
Preferred
- Experience with high-user-volume platforms such as digital gaming, e-commerce, or finance
- Deep understanding of real-time risk management, recommendation systems, VIP scoring, and anomaly detection
- Engineering mindset with experience in productizing and deploying models to production
- Knowledgeable in data intellectual property rights, security, and ethics (data as a core enterprise asset)
Education
Master's degree or higher in Mathematics, Statistics, Computer Science, Economics, or related fields
EA License No: 96C4864
CEI Reg No: R1873093 (LOH CHIANG SOON)