Lead Machine Learning Researcher

Do you want to take the first step in making Filipinos’ lives better everyday? Here in GCash we want to stay at the forefront of the FinTech industry by creating innovative, meaningful, and convenient financial solutions for the nation! G ka ba? Join the G Nation today!

You Will Be Responsible For The Following

• Partner with stakeholders to translate ambiguous business or product problems into well-defined machine learning research questions and success metrics.
• Survey literature and state-of-the-art techniques to propose appropriate algorithms and model architectures for each use case.
• Collect, explore, and prepare data (feature engineering, data cleaning, labeling strategies) to support robust experimentation.
• Design and run systematic experiments, including baselines, ablation studies, and hyperparameter tuning, ensuring results are statistically sound and reproducible.
• Develop and evaluate models using appropriate metrics, with careful attention to fairness, robustness, and explainability.
• Document methodology and findings in clear, structured reports and presentations for both technical and non-technical audiences.
• Collaborate with ML engineers and data engineers to productionize models, define interfaces, and support integration into products and workflows.
• Monitor model performance post-deployment and propose retraining, recalibration, or new approaches as data and business conditions evolve.
• Contribute to internal tools, libraries, and best practices that raise the bar for machine learning and research quality across the team.
• Mentor peers and share knowledge through code reviews, demos, and internal talks on new techniques, tools, or research findings.
  
  
  

We Are Looking For

• Strong foundation in machine learning, statistics, and optimization, with the ability to reason about trade-offs between different approaches.
• Proficiency in Python and common ML libraries/frameworks (e.g., NumPy, pandas, scikit-learn, PyTorch and/or TensorFlow).
• Hands-on experience building and evaluating models on real-world datasets (e.g., tabular, time series, NLP, or recommender systems).
• Solid understanding of the end-to-end ML lifecycle: problem framing, data preparation, modeling, validation, and post-deployment monitoring.
• Experience working with large datasets and modern data platforms (e.g., SQL, data warehouses, notebooks, experiment tracking).
• Ability to communicate complex technical ideas clearly, both in writing and in presentations, to technical and non-technical stakeholders.
• Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related field (or equivalent practical experience).
  
  
  

What We Offer

Opportunity for career growth and development in the #1 FinTech company in the country Working with a dynamic and highly collaborative team who want to change the game A company that values their people with highly competitive and flexible compensation and benefits package

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