Data Scientist - Fraud Strategy and Analytics

About GoTyme

GoTyme is a joint venture between the Gokongwei Group, one of the biggest conglomerates in the Philippines, and the Singapore-headquartered digital banking group Tyme. This venture combines the trusted Gokongwei brand, customer base, and distribution ecosystem with Tyme’s globally proven digital banking technology and hands-on experience building South Africa’s leading digital bank, TymeBank, one of the fastest-growing digital banks in the world today.

At GoTyme, we have embarked on a journey to democratize financial services and bring next-level banking to the Philippines. We seek individuals who share our belief that the game is worth changing, to join our growing team of GoTymers as we build, launch, and scale a bank that empowers all Filipinos to navigate a path to financial freedom.

About the role

We are looking for a highly analytical and detail-oriented Analyst / Senior Analyst – Fraud Strategy & Analytics to join our Fraud Analytics Team . You will play a key role in developing, implementing, and optimizing fraud prevention and detection strategies across GoTyme Bank’s products and digital channels. You will analyze complex datasets, identify emerging fraud trends, and work closely with cross-functional teams to balance fraud loss mitigation with customer experience.

• Fraud Strategy & Analytics
• Monitor transactional and behavioral data to identify patterns indicative of fraudulent activity.
• Develop and enhance rule-based and model-driven fraud detection strategies for digital banking products (e.g., cards, transfers, deposits, lending).
• Conduct deep-dive investigations into fraud cases and recommend preventive actions.
• Partner with product, technology, and data teams to enhance fraud monitoring systems and controls.
• Support the design, testing, and rollout of new fraud mitigation tools and analytics capabilities.
• Prepare regular performance reports and present fraud insights to senior management.
• Participate in incident reviews, risk assessments, and process improvement initiatives.
• Innovation and Research
• Experiment with new tools, techniques, algorithms and methodologies to enhance the bank’s capabilities. 

Requirements

Must Haves

• Bachelor’s degree or higher in applied mathematics, statistics, computer science, physics, data science, economics, engineering or equivalent quantitative discipline.
• 2–5 years of experience in fraud analytics or credit strategy or analytics within banking, fintech, or payments.
• Experience in first-party, third-party, or synthetic fraud risk management is preferred.
• Knowledge of machine learning techniques for fraud detection is preferred.
• Strong SQL for data manipulation and reporting.
• Proficiency in Python for analytics, modeling, and automation.
• Exposure to AI/ML frameworks (advantageous but not required).
• Proficiency in Excel, and/or data visualization tools (e.g., Tableau, PowerBI).
• Strong analytical problem-solving skills and attention to detail.
• Excellent communication skills – able to present complex findings in a clear, concise manner.
• Experience in fintech, digital banking, or technology-driven financial institutions preferred.