Data Science for Decision Engine
āļ›āļāļīāļšāļąāļ•āļīāļ‡āļēāļ™āļĒāļąāļ‡ āļšāļĢāļīāļĐāļąāļ— 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
  • Bachelor’s 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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āļ›āļĢāļ°āļŠāļšāļāļēāļĢāļ“āđŒāļ—āļĩāđˆāļˆāļģāđ€āļ›āđ‡āļ™
  • 3 āļ›āļĩ
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  • āļŠāļēāļĄāļēāļĢāļ–āļ•āđˆāļ­āļĢāļ­āļ‡āđ„āļ”āđ‰
āļŠāļēāļĒāļ‡āļēāļ™
  • āđ„āļ­āļ—āļĩ / āđ€āļ‚āļĩāļĒāļ™āđ‚āļ›āļĢāđāļāļĢāļĄ
  • āļžāļąāļ’āļ™āļēāļ˜āļļāļĢāļāļīāļˆ
  • āļ™āļąāļāļ§āļīāđ€āļ„āļĢāļēāļ°āļŦāđŒ
āļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™
  • āļ‡āļēāļ™āļ›āļĢāļ°āļˆāļģ

āđ€āļāļĩāđˆāļĒāļ§āļāļąāļšāļšāļĢāļīāļĐāļąāļ—

āļˆāļģāļ™āļ§āļ™āļžāļ™āļąāļāļ‡āļēāļ™:2000-5000 āļ„āļ™
āļ›āļĢāļ°āđ€āļ āļ—āļšāļĢāļīāļĐāļąāļ—:āļāļēāļĢāđ€āļ‡āļīāļ™āđāļĨāļ°āļāļēāļĢāļ˜āļ™āļēāļ„āļēāļĢ
āļ—āļĩāđˆāļ•āļąāđ‰āļ‡āļšāļĢāļīāļĐāļąāļ—:āļāļĢāļļāļ‡āđ€āļ—āļž
āđ€āļ§āđ‡āļšāđ„āļ‹āļ•āđŒ:www.krungthai.com
āļāđˆāļ­āļ•āļąāđ‰āļ‡āđ€āļĄāļ·āđˆāļ­āļ›āļĩ:1966
āļ„āļ°āđāļ™āļ™:4/5

āļ˜āļ™āļēāļ„āļēāļĢāđƒāļŦāđ‰āļ„āļ§āļēāļĄāļŠāļģāļ„āļąāļāļāļąāļšāļāļēāļĢāļšāļĢāļīāļŦāļēāļĢāļ—āļĢāļąāļžāļĒāļēāļāļĢāļšāļļāļ„āļ„āļĨāđ€āļ›āđ‡āļ™āļ­āļĒāđˆāļēāļ‡āļĒāļīāđˆāļ‡ āđ„āļĄāđˆāļ§āđˆāļēāļˆāļ°āđ€āļ›āđ‡āļ™āļāļēāļĢāļŠāļĢāļĢāļŦāļēāļšāļļāļ„āļĨāļēāļāļĢāļ—āļĩāđˆāļĄāļĩāļ„āļ§āļēāļĄāļĢāļđāđ‰āļ„āļ§āļēāļĄāļŠāļēāļĄāļēāļĢāļ– āļāļēāļĢāļžāļąāļ’āļ™āļēāļžāļ™āļąāļāļ‡āļēāļ™āļ”āđ‰āļ§āļĒāļŦāļĨāļąāļāļŠāļđāļ•āļĢāļ•āđˆāļēāļ‡āđ† āļ•āļĨāļ­āļ”āļˆāļ™āļˆāļąāļ”āļāļēāļĢāļāļķāļāļ­āļšāļĢāļĄ āđ€āļžāļ·āđˆāļ­āđƒāļŦāđ‰āļŠāļ­āļ”āļ„āļĨāđ‰āļ­āļ‡āļāļąāļšāļŠāļ āļēāļžāđāļ§āļ”āļĨāđ‰āļ­āļĄāļ—āļĩāđˆāđ€āļ›āļĨāļĩāđˆāļĒāļ™āđāļ›āļĨāļ‡āđ„āļ› āļ­āļĩāļāļ—āļąāđ‰āļ‡āļĒāļąāļ‡āļŠāđˆāļ‡āđ€āļŠāļĢāļīāļĄāđƒāļŦāđ‰āļžāļ™āļąāļāļ‡āļēāļ™āļžāļąāļ’āļ™āļēāļ•āļ™āđ€āļ­āļ‡āļ­āļĒāļđāđˆāđ€āļŠāļĄāļ­āļ‹āļķāđˆāļ‡āđ€āļ›āđ‡āļ™āđ„āļ› āļ•āļēāļĄāļ™āđ‚āļĒāļšāļēāļĒāļāļēāļĢāđ€āļ›āđ‡āļ™āļ­āļ‡āļ„āđŒāļāļĢāđāļŦāđˆāļ‡āļāļēāļĢāđ€āļĢāļĩāļĒ ...

āļ­āđˆāļēāļ™āļ•āđˆāļ­

āļĢāđˆāļ§āļĄāļ‡āļēāļ™āļāļąāļšāđ€āļĢāļē:

Today, we are recruiting qualified people in various positions. Especially, Authorized teller who works at bank branches. If you are a competent and interested to be a part of the major factor for organization to meet ambition, we appreciate to review your qualifications for employment with Kungt ...

āļ­āđˆāļēāļ™āļ•āđˆāļ­

āļŠāļģāļ™āļąāļāļ‡āļēāļ™āđƒāļŦāļāđˆ: āđ€āļĨāļ‚āļ—āļĩāđˆ 35 āđāļ‚āļ§āļ‡āļ„āļĨāļ­āļ‡āđ€āļ•āļĒāđ€āļŦāļ™āļ·āļ­ āđ€āļ‚āļ•āļ§āļąāļ’āļ™āļē āļāļ—āļĄ. 10110
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āļ”āļđāļ‡āļēāļ™āļ—āļąāđ‰āļ‡āļŦāļĄāļ” >

āļ—āļĩāđˆ WorkVenture āđ€āļĢāļēāđƒāļŦāđ‰āļĄāļđāļĨāđ€āļŠāļīāļ‡āđ€āļāļĩāđˆāļĒāļ§āļāļąāļšāļšāļĢāļīāļĐāļąāļ— āļ˜āļ™āļēāļ„āļēāļĢāļāļĢāļļāļ‡āđ„āļ—āļĒ āļˆāļģāļāļąāļ” (āļĄāļŦāļēāļŠāļ™) āđ‚āļ”āļĒāļĄāļĩāļ‚āđ‰āļ­āļĄāļđāļĨāļ—āļĩāđˆāđ€āļāļĩāđˆāļĒāļ§āļ‚āđ‰āļ­āļ‡ āļ•āļąāđ‰āļ‡āđāļ•āđˆāļ āļēāļžāļšāļĢāļĢāļĒāļēāļāļēāļĻāļāļēāļĢāļ—āļģāļ‡āļēāļ™ āļĢāļđāļ›āļ–āđˆāļēāļĒāļ‚āļ­āļ‡āļ—āļĩāļĄāļ‡āļēāļ™ āđ„āļ›āļˆāļ™āļ–āļķāļ‡āļĢāļĩāļ§āļīāļ§āđ€āļŠāļīāļ‡āļĨāļķāļāļ‚āļ­āļ‡āļāļēāļĢāļ—āļģāļ‡āļēāļ™āļ—āļĩāđˆāļ™āļąāđˆāļ™ āļ‹āļķāđˆāļ‡āļ‚āđ‰āļ­āļĄāļđāļĨāļ—āļļāļāļ­āļĒāđˆāļēāļ‡āļšāļ™āļŦāļ™āđ‰āļēāļ‚āļ­āļ‡āļšāļĢāļīāļĐāļąāļ— āļ˜āļ™āļēāļ„āļēāļĢāļāļĢāļļāļ‡āđ„āļ—āļĒ āļˆāļģāļāļąāļ” (āļĄāļŦāļēāļŠāļ™) āļĄāļĩāļžāļ™āļąāļāļ‡āļēāļ™āļ—āļĩāđˆāļāļģāļĨāļąāļ‡āļ—āļģāļ‡āļēāļ™āļ—āļĩāđˆāļšāļĢāļīāļĐāļąāļ— āļ˜āļ™āļēāļ„āļēāļĢāļāļĢāļļāļ‡āđ„āļ—āļĒ āļˆāļģāļāļąāļ” (āļĄāļŦāļēāļŠāļ™) āļŦāļĢāļ·āļ­āđ€āļ„āļĒāļ—āļģāļ‡āļēāļ™āļ—āļĩāđˆāļ™āļąāđˆāļ™āļˆāļĢāļīāļ‡āđ† āđ€āļ›āđ‡āļ™āļ„āļ™āđƒāļŦāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļˆāļĢāļīāļ‡āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļŠāļ™āļŠ.āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļĄāļ­āļĢāđŒ āļŠāļ•āļđāļ”āļīāđ‚āļ­āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļ„āļĢāļĩāđ€āļ­āđ€āļˆāļ­āļĢāđŒāđāļĨāļšāļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ WV