Data Science for Decision Engine
āļāļĩāđāļāļāļēāļāļēāļĢāļāļĢāļļāļāđāļāļĒ āļāļģāļāļąāļ (āļĄāļŦāļēāļāļ)Data Science for Decision Engine
āļāļāļīāļāļąāļāļīāļāļēāļāļĒāļąāļ āļāļĢāļīāļĐāļąāļ Infinitas by Krungthai
We are seeking a Data Scientist specializing in credit decision engine development to drive data-driven lending decisions and risk assessment. The ideal candidate will combine strong analytical skills with deep understanding of credit process.
Key Responsibilities
Model Development & Deployment
- Develop and maintain credit risk assessment and lending decisions modules
- Expertise for handling large financial databases and credit data manipulation
- Design and implement credit-risk decisioning model solutions using API-based frameworks (e.g., Flask, Fast API) or event-driven architecture (e.g., Kafka, Pub/Sub), along with other suitable technologies
- Monitor model performance to ensure high accuracy and reliability in credit decisions Data Analysis & Risk Assessment
- Clean and preprocess financial datasets, particularly credit lending and risk data
- Conduct advanced statistical analyses to support risk assessment and lending decisions
Required Qualifications
Technical Skills
- Proficiency in Python, SQL, and machine learning libraries (TensorFlow, PyTorch, Scikit-Learn)
- Experience with cloud platforms (AWS,GCP) for model deployment
- Knowledge of statistical and machine learning techniques for risk modeling Domain Expertise
- Understanding of credit lending and risk assessment principles
- Experience in financial data analysis within regulatory constraints
- Proven track record in developing credit decision engines (optional)Education & Experience
- Bachelorâs degree or higher in Statistics, Computer Science, Mathematics, or related field
- Minimum 3 years experience in retail lending or similar role Additional Requirements
- Strong communication skills for presenting complex findings and process flow to management
- Experience with data visualization tools (Tableau, Power BI)
- Ability to work collaboratively with cross-functional teams
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