Senior Risk Analytics Engineer

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

The Senior Risk Analytics Engineer serves as the data engineering and analytics backbone of the GoTyme Bank Risk Management function, operating as a critical second-line resource that transforms raw risk data into structured insights, automated pipelines, and actionable intelligence. Credit Risk is the primary domain focus of this role, covering ECL modelling support, credit portfolio analytics, regulatory reporting support, and lending portfolio monitoring. The role works in close day-to-day partnership with, and is complemented by, a Senior Credit Risk Manager who brings domain expertise and credit risk judgement to the function, and overall accountability for all risk domains sitting with the Chief Risk Officer. Beyond Credit Risk, the role carries equally important responsibilities supporting other key risk domains such as Operational Risk, Financial Risk, Model Risk, Business Continuity, Third-Party Risk management.

Sitting within the 2nd Line of Defence, the role provides independent data assurance and challenge to 1st-line-generated data, builds and maintains the Risk team's integrated monitoring and reporting infrastructure, and develops the automation frameworks that drive scalable, data-driven risk oversight. The Senior Risk Analytics Engineer works as a close analytical partner to the Senior Credit Risk Manager and reports to the Director of Risk, collaborating across Model Risk, Finance, Technology Risk, and other enterprise stakeholders to ensure that risk reporting across all domains is timely, accurate, and regulatory-ready, supporting BSP compliance obligations and enabling informed risk decisions across the bank.

• Credit Risk Analytics
• Run ECL calculations and checks, and support the progressive automation of ECL computations as the models mature; provide data support for challenger models and overlays
• Maintain and enhance vintage analysis, roll rate reporting, and delinquency (DPD) tracking views; ensure data integrity between 1st-line-generated and 2nd-line-validated figures
• Validate and challenge first-line regulatory submissions and other credit-related financial reports (monthly/quarterly), reviewing for completeness, consistency, and alignment with policy definitions
• Monitor credit portfolio metrics, including Risk Appetite Statement (RAS) triggers, KRIs, and early delinquency signals, and generate required data for Credit Risk Committee (CRC) and ALCO pack preparation
• Run economic capital (EC) model for Credit Risk; support scenario and stress-testing analyses for the credit portfolio as required
• Support loan management system data quality validations on a quarterly basis as 2nd-line assurance
• Develop and maintain data analytics capabilities to support increasing product complexity and portfolio growth from GoTyme's lending business (salary loans, buy-now-pay-later, and beyond), in line with BSP supervisory expectations
• Multi-Risk Analytics, Engineering and Platform
• Generate KRI and monitoring metrics across Operational Risk, Financial Risk (ILM, NII, EVE, VaR), Model Risk, and Third-Party Risk; automate recurring computations and tracking where applicable
• Support periodic risk scanning routines across Slack, email, and other internal channels to support Operational Risk monitoring and assurance
• Run EC models for Operational Risk and Financial risks; assist with 2nd-line investigations involving data extraction and analysis in risk tools and analytical platforms as needed
• Develop and maintain EWI alert coding and automation for Operational Risk, Financial Risk, and geopolitical early warning signals; support the geopolitical risk monitoring framework with data-driven indicator feeds
• Support the development and maintenance of the bank's integrated risk management platform and associated data infrastructure, including BCM data centralisation, JIRA workflow updates, data pipeline improvements, and documentation of data models and automated processes to support model risk and BSP audit requirements
• Act as the Risk team's champion for AI and Automation by coordinating risk use cases, providing data requirements for automated workflows, and supporting team upskilling on data and analytics tools
• Regulatory Compliance and Data Governance
• Ensure data outputs and risk metrics are compliant with BSP regulatory requirements, including operational risk, reporting governance, and model risk provisions under the relevant MORB sections and forthcoming BSP Model Risk Management (MRM) guidelines; support internal audit and BSP examination requests with timely, well-documented data extracts
• Provide 2nd-line challenge and independent review of data quality across all risk reporting submissions, identifying data gaps, anomalies, or inconsistencies and escalating as appropriate
• Maintain a structured inventory of data sources, reporting processes, and automated workflows to support governance, change control, and model documentation standards
  
  

Requirements

• Bachelor's degree in Statistics, Mathematics, Computer Science, Data Science, Information Technology, Engineering, or a related quantitative discipline
• A degree in Finance, Economics, or Business with demonstrated strong quantitative and technical skills will also be considered
• At least 5 years of experience in data analytics, risk analytics, or data engineering within banking, fintech, digital banking, or financial services; a combination of domain and technical experience across these areas is equally welcome
• Experience in credit risk analytics is strongly preferred, having familiarity with ECL/IFRS 9 concepts, credit portfolio monitoring, roll rates, or regulatory credit reporting will be a significant advantage; candidates who built this knowledge through self-directed learning or adjacent roles are encouraged to apply
• Demonstrated experience building automated data pipelines, dashboards, and reporting frameworks in a production environment; prior use of Databricks is highly advantageous
• Exposure to multi-risk environments (operational risk, financial risk, model risk) in a 2nd-line or independent review/assurance capacity is a strong plus
• Experience working with central bank-regulated institutions, and familiarity with Risk-related regulatory requirements and accounting reporting requirements
• Exposure to risk management platforms, GRC tools, or integrated risk systems is an advantage but not required
• Proficiency in Python and/or R for data processing, statistical analysis, modelling, or automation, along with strong SQL skills (e.g., Databricks, Spark SQL, PostgreSQL). Candidates stronger in one area are welcome
• Experience building or contributing to data pipelines, ETL/ELT workflows, or automated reporting; familiarity with Databricks is a plus but not required
• Working knowledge of credit risk analytics (ECL/IFRS 9, roll rates, vintage analysis, delinquency monitoring, PD/LGD/EAD), from formal roles or self-developed expertise
• Familiarity with financial risk metrics (VaR, Stressed VaR, ILM, NII, EVE) and ability to translate them into code-driven solutions
• Experience with data visualisation and dashboard tools (e.g., Tableau, Power BI, Grafana, Databricks dashboards)
• Proficiency in version control and collaboration tools (Git, JIRA, Confluence)