HEAD OF DATA SCIENCE （IT/Software House)

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) J-18808-Ljbffr

Location: Singapore, SG

Posted Date: 6/15/2025