Data Scientist - MCA

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 Data Scientist will play a pivotal role in assessing, analysing, and mitigating credit risks within the MCA Credit Analytics team at GoTyme Bank Philippines. Working end-to-end from data exploration through to production-aligned features and monitoring, the candidate will use data, feature engineering, and experimentation to improve credit decisioning and portfolio performance for our Merchant Cash Advance product.

Credit Risk Modelling

• Develop, implement, and maintain acquisition scorecards and models to evaluate MCA applicants.
• Build and iterate credit risk features and model inputs (behavioural signals, affordability proxies, stability-tested transformations), partnering closely with senior modellers and engineering.
• Contribute to the development and improvement of predictive models using modern machine learning approaches, with a focus on robustness, stability, and deployability.
• Monitor provision models aligned with regulatory and accounting standards.
• Enhance portfolio monitoring tools to track credit performance and early warning signals, including drift, stability, segment performance, and data quality checks.

Data Analysis & Insights

• Analyse customer, transactional, repayment, and business health data to identify drivers of risk, loss, approval rates, and customer outcomes.
• Identify trends, correlations, and anomalies that impact credit performance and portfolio stability.
• Support portfolio analytics: vintage analysis, roll-rates, migration, early warning indicators, collections funnel analytics, and loss driver deep-dives.
• Collaborate with product, finance, and operations teams to embed data-driven decision-making.

Credit Policy & Experimentation

• Design, run, and evaluate credit policy experiments (cut-offs, limits, pricing/risk trade-offs, segment strategies), including post-implementation reviews.
• Develop segmentation and behavioural models to drive proactive portfolio management.
• Support stress testing and scenario analysis.

Innovation & Automation

• Design and deploy machine learning models for predictive credit risk assessment.
• Leverage advanced analytics to streamline underwriting and risk monitoring processes.
• Continuously explore new data sources and analytical methods to improve risk evaluation.
• Work with Data/Engineering to improve data definitions, quality, lineage, and reproducible pipelines; document feature logic and assumptions.

Governance & Documentation

• Contribute to governance documentation including model inputs, feature catalogues, monitoring evidence, and change logs.
• Ensure all modelling work meets internal standards and applicable BSP regulatory requirements.

Requirements

Must haves

• Degree in Data Science, Statistics, Mathematics, or a related quantitative field.
• Familiarity with BSP credit risk guidelines and IFRS 9 is advantageous.
• 3+ years of experience in data science, credit analytics, or credit risk management within a bank, fintech, lender, or consulting environment.
• Strong background in statistical modelling, machine learning, and predictive analytics.
• Proficiency in Python and/or SQL; familiarity with R is an advantage.
• Experience building and validating credit risk models, including scorecards and provisioning models.
• Solid grounding in predictive model evaluation - ranking performance, calibration, and stability - and business impact measurement.
• Exposure to advanced machine learning concepts (ensemble methods, cross-validation, hyperparameter tuning) and the ability to apply them responsibly in production settings.
• Strong business acumen with the ability to communicate insights to both technical and non-technical stakeholders.
• Curious and pragmatic, focused on measurable outcomes; comfortable working in detail and iterating quickly while maintaining quality.
• Collaborative and able to work across markets and time zones.
• Experience in SME lending, merchant cash advances, or alternative credit products.
• Familiarity with IFRS 9, Basel, or BSP-equivalent credit risk regulatory frameworks.
• Experience with bureau data, open banking/transactional data, device/behavioural signals, or alternative data sources.
• Exposure to cloud-based data platforms (Databricks, BigQuery, Snowflake, AWS, GCP, or Azure) and version control (Git).
• Familiarity with model monitoring, governance, and documentation practices in regulated environments.
• Knowledge of model interpretability methods (e.g., SHAP, LIME).