Data Scientist (Lending) (Jakarta Pusat)

Requirements - At least a Bachelor's or Master's degree with an excellent GPA in Mathematics, Statistics, Computer Science, Risk Management, or Engineering. - Minimum of 2+ years of experience in financial service data modeling, including deploying, building, and maintaining models in production using data modeling tools. Certification in relevant areas is a plus. - Proficiency in SQL and Python programming, with knowledge of Statistical/Machine Learning algorithms such as Logistic Regression, Gradient Boosting (XGBoost/CatBoost), and KMeans. - Strong mathematical, modeling, and reasoning skills to understand and accurately deploy Credit Scoring/Propensity models, utilizing them to provide actionable recommendations. - Familiarity with retail and commercial loans in the financial industry, including knowledge of credit risk and business processes. Experience in data profiling, attribute mapping, ETL processes, and data governance. - Demonstrated commitment to delivering high-quality work and exceeding expectations, particularly in fast-paced environments. - Excellent oral and written communication skills in both Indonesian and English, with the ability to effectively communicate technical terms to various stakeholders. Description - Develop and implement robust credit risk models, including acquisition scorecards, behavioral scorecards, and collection scorecards, for the cash loan product in order to support the business. - Explore the use of alternative data sources to enhance the performance of the scorecard model, in addition to traditional credit data. - Continuously monitor, improve, and validate the performance of the models, while also researching the latest modeling methodologies and best practices in the industry. Ensure compliance with all regulatory requirements. - Develop and maintain enterprise risk management models, such as Basel, IFRS 9, PD, LGD, EAD models, stress testing models, and macroeconomic models. - Conduct benchmarking and industry research to review changes in regulatory requirements for managing credit risk and calculating risk-weighted assets (RWA) more effectively. - Continually identify credit and business issues and employ scoring, modeling, and analytics tools to find solutions or make quality improvements. Skills - Attention to Detail - Problem Solving - Communication Skills - Teamwork - Analytical Skills - Analytics - Statistical Analysis - Statistics - Risk Management - Engineering - SQL - Python - Retail Management - English Language