Staff Data Scientist

The Opportunity: Staff Data Scientist

We’re hiring for a Staff Data Scientist to help expand Tilt’s credit modeling and risk analytics capability for our Philippines-based business, Cashalo.

This is a high impact role with a direct mandate to build and refine underwriting models as well as other models, working at the intersection of credit, data science, and leadership.

You’ll collaborate with a lean but growing team, applying advanced modeling techniques to predict loss performance, leverage alternative data sources, and build next-generation tools that shape credit access for millions.

This is a 100% remote role that requires travel 2-4 times/year.

How You’ll Make an Impact

• Cashalo is undertaking a solid turn in unit economics with a view to growing to being the largest fintech in PH, and this is an opportunity to build foundational credit models and features from the ground up.

• With direct exposure to leadership, your work will shape both near-term lending performance and long-term data infrastructure.

• You will partner directly with cross-functional teams across credit, engineering, and leadership, bringing strong project and stakeholder management skills to a lean, fast-growing team.

• Work with alternative data where the underwriting process and modeling reflect the maturity of the market

Why You’re a Great Fit

• Deep expertise in predictive modeling, ideally within credit risk or actuarial settings.

• Strong proficiency in Python, SQL, and ML libraries like scikit-learn, LightGBM, XGBoost.

• Familiarity with AI tools such as GitHub Copilot or LLM-based assistants.

• Experience building models from scratch.

• Strong stakeholder management skills; experience partnering with cross-functional teams to drive outcomes.

• Clear communication skills, including the ability to present complex findings to non-technical leadership.

• Experience working in high-growth, ambiguous environments with lean teams.

Nice-to-haves

• Fintech experience - especially in credit/lending models or alternative underwriting.

• Familiarity with legacy credit systems and how to modernize them.

• Familiarity with funnel management and ways to novate reject rules into modeling outcomes

• Exposure to the Philippines' credit markets and ability to transfer insights between geographies.

• Experience with models across the loan life cycle

• Knowledge of deep learning frameworks (TensorFlow, PyTorch) and modelling (CNN, RNN) — a plus but not essential.