Data Scientist

Job Descriptions:

• Partner with business stakeholders across BOL Company to solve analytical problems and support strategic decision-making.
• Design and analyze experiments (A/B tests), including hypothesis formulation, metric definition, impact evaluation, and interpretation.
• Develop and apply statistical and machine learning models (e.g., regression, tree-based models, forecasting) for use cases such as credit risk, default prediction, customer segmentation, and operational optimization.
• Build forecasting models, including probability of default (PD) and risk scoring frameworks, to support financial and credit-related decisions.
• Deliver clear, actionable insights and recommendations through presentations and stakeholder discussions.
• Develop practical data science solutions, including batch processing pipelines and API-based model deployment (e.g., LLM integrations).
• Manage multiple projects simultaneously in a fast-paced, dynamic environment.

Job Qualifications:

• Bachelor’s or Master’s degree in Statistics, Data Science, Computer Engineering, Computer Science, Econometrics, or a related quantitative field.
• At least 1 year of experience in data analytics, data science, or quantitative modeling roles (New Graduated are welcome).
• Strong analytical thinking with solid business acumen.
• Strong foundation in probability, statistics, machine learning, and AI.
• Proficiency in SQL and Python, with strong coding skills for data manipulation, modeling, and deployment.
• Experience with experimentation and A/B testing frameworks.
• Experience in building predictive models, particularly in risk modeling or default probability estimation, is highly preferred.
• Excellent English communication skills, with the ability to independently present and discuss insights with stakeholders.
• Strong collaboration skills and ability to manage multiple priorities.
• Experience in credit risk modeling, financial data science, or related domains.
• Familiarity with forecasting techniques and risk scoring methodologies.
• Experience in deploying AI/LLM-driven solutions in business environments.