Job Description

It's fun to work in a company where people truly BELIEVE in what they're doing!

‎ 

‎ 

Job Description

Our data science team is responsible for solving complex problems and optimizing various aspects of Traveloka’s business. You will get a wide range of exciting opportunities to bring impact by helping to make decisions at scale across our various business lines, and get to work with the brightest minds in the industry. Join us in revolutionizing the travel industry through data!

Key Responsibilities:

• Partner with Risk, Product, Anti-Fraud, and Business Teams to understand risk challenges and define data-driven strategies.

• Develop and enhance risk models for Probability of Default (PD)/Loss Given Default (LGD)/Exposure at Default (EAD) and anti-fraud models using machine learning and statistical techniques.

• Design, validate, and maintain risk-based labels (e.g., DPD buckets, NPL status) to ensure consistency and reliability in model training.

• Optimize credit strategies using simulation techniques to assess the impact of new policies on loan book performance.

• Work closely with Data Engineering teams to ensure high-quality, scalable data pipelines for real-time/scheduled model predictions and performance monitoring.

• Oversee and manage multiple credit risk modeling projects, ensuring alignment with business objectives, timelines, and regulatory requirements.

• Communicate insights effectively to both technical and non-technical stakeholders, including risk managers and senior executives.

‎ 

Requirements

Qualifications:

• Bachelor’s in a quantitative field (Computer Science, Data Science, Statistics, Economics, Finance, or related).

• 3+ years of experience in data science, preferably with a focus on credit risk modeling, loan portfolio analytics, or financial risk assessment.

• Strong knowledge of credit risk/anti-fraud in the consumer lending domain.

• Expertise in Python and machine learning/statistics libraries (scikit-learn, LGBM, XGBoost, etc).

• Proficiency in SQL and data processing libraries for analyzing large-scale credit and transaction datasets.

• Experience with cloud-based technologies (GCP preferred) and Docker for scalable model deployment.

• Ability to translate business objectives into data-driven risk strategies and communicate findings effectively.

Good to Have:

• Experience with Deep Learning frameworks (PyTorch, TensorFlow) for alternative credit risk modeling.

• Familiarity with alternative data sources for credit scoring (e.g., transaction data, behavioral data).

• Knowledge of real-time credit decisioning systems and implementation of adaptive risk strategies.

• Experience with A/B testing for credit policy optimization.

‎ 

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

Job Requirements