Job Title: Data Scientist

Experience: 3 to 6 years of relevant data science experience

About the Job:

We are looking for Data Scientists who can independently own a modelling workstream and deliver practical improvements to Tonik's lending decisions. Depending on allocation, you will work on cash or installment-loan underwriting, behavioural and offer scores, collections, payroll-backed lending, fraud, offer propensity or income prediction.

You will develop and validate models, investigate performance changes and work with MLE to bring approved models into production. The team is moving toward risk-based refreshes, generally every three to six months for established scorecards, supported by timely monitoring and stronger development discipline.

Job Responsibilities:

- Own assigned modelling workstreams from business problem definition and data preparation through feature engineering, model development, validation and production handover. Agree the target, population, success measures and delivery plan with stakeholders.

- Build features from application, demographic, bureau, device, event, transaction, repayment and alternative data. Use Python and SQL to create reproducible datasets and verify joins, missing values, timestamps and customer or loan identifiers.

- Develop and compare scorecards and machine learning models using appropriate statistical and gradient boosting methods. Evaluate component scores, stacked models and segment-specific challengers against credible baselines.

- Design temporal and out-of-time validation, prevent target leakage, and ensure repayment outcomes have matured. Identify distortions caused by restructuring, account freezes, approval policy changes or changing collection strategies.

- Evaluate discrimination, calibration, stability and segment performance using Gini, KS, PSI, CSI and relevant business measures. For the assigned use case, relate model performance to credit losses, recoveries, attrition, offer uptake or prediction error.

- Investigate deterioration and recommend an evidence-based response, including correcting data defects, recalibrating, retraining or redeveloping a model. Explain the effects of changing customer populations and business policies.

- Execute planned refreshes and respond to priority risk issues. Maintain model documentation, feature specifications, validation results and reproducible experiments so the work can be reviewed and repeated.

- Partner with MLE on feature and inference contracts, test cases and pre-release score reconciliation.



Support user acceptance testing and post-release checks for real-time underwriting and batch scoring workflows.

- Define monitoring requirements with the DAnR dashboard developer and Risk teams, including cohort definitions, maturity rules, thresholds and segment views. Work with the Data Program Manager on vendor data issues and new-source backtests.

- Participate in code and model reviews, seek technical challenge from Lead Data Scientists, and communicate progress, blockers and findings proactively across Chennai, Manila and Singapore.

- Adhere to model-governance and regulatory documentation standards for every model you build or refresh.

Required Experience and Qualifications:

- 3 to 6 years of hands-on data science experience, including practical experience developing credit risk or lending models in a bank, fintech, NBFC or a business delivering lending analytics.

- A bachelor's or master's degree in statistics, mathematics, computer science, data science, engineering or a related quantitative discipline.

- Strong Python and SQL skills, with experience in pandas, NumPy, scikit-learn and at least one gradient boosting library such as XGBoost or LightGBM.

- Sound understanding of classification, probability estimation, feature selection, class imbalance, calibration and temporal validation. Ability to interpret scorecard performance and stability metrics correctly.

- Experience working with large datasets, developing reproducible analytical code, and collaborating with engineers to put models into production. Working knowledge of Git and code review practices.

- Ability to manage an individual workstream, document assumptions and limitations, and explain analytical findings clearly to Risk and business stakeholders.

Preferred Experience and Qualifications:

- Experience with behavioural scores, collections, payroll attrition, out-of-pocket repayment risk, offer propensity, fraud or income estimation.

- Experience with alternative data, stacked scorecards, segmentation, survival analysis or model explainability techniques such as SHAP.

- Experience with GCP and Big Query, cloud analytical platforms, experiment tracking and production monitoring.





- Exposure to frequent model refreshes and modelling in populations affected by changing economic conditions or approval policies.

What Success Looks Like:

- Assigned models and refreshes are delivered with sound validation, reproducible code and clear handover documentation.

- Performance deterioration is investigated promptly, with recommendations that distinguish model issues from data or policy effects.

- Production scores reconcile with approved model outputs, and monitoring definitions are agreed before release.

Who are we looking for?

We value teamwork, honesty, reliability, practical problem-solving and a sense of humour. You take ownership of your work, communicate early when something is wrong, and challenge assumptions respectfully. You are comfortable collaborating across locations and improving how the team works.

- Willingness to go the extra mile for success.

- Energetic and lively, and street smart.

- As our approach to banking is far from ordinary, we are looking for an innovative and creative individual to join our team.

- Above all, a outstanding team player and proactive communicator. This is a table-stake criterion.

About Tonik:

We are a transformative digital bank with a mission to revolutionize the way money works. We create better financial products for the world by encouraging inclusivity through simplifying processes, reducing costs, improving transparency, and embracing flexibility to empower more people with unique financial needs. All these are on the most secure mobile banking platform.

To enable this, innovation is woven into the DNA of our company. We do not just search for the best talent, but for people who seek to challenge the status quo and are passionate about transforming the impossible into reality.

Tonik (www.tonikbank.com) is the first digital-only neo bank in the Philippines, providing loan, deposit, payment, and card products to consumers on a highly secure digital banking platform. The neo bank operates based on the first private digital bank license issued by the Bangko Sentral ng Pilipinas. Tonik is led by a team of retail finance veterans who have previously built and scaled multiple retail banks and fintechs across the global emerging markets.

It is backed by top international venture capital funds. Tonik operates out of hubs in Singapore (HQ), Manila, Chennai, and Kyiv.

Our Data team works with Risk, Product, Technology and business teams to improve lending decisions and customer outcomes through reliable data, analytics and machine learning.

📌 Data Scientist (Chennai)
🏢 Tonik
📍 Chennai