Credit Data Scientist
2 weeks ago
Location: Jakarta, Indonesia
About the Role
Join an innovative team driving growth by building credit models for groundbreaking products in Southeast Asia. This role involves developing advanced solutions, collaborating across teams, and solving complex problems to create cutting-edge credit products. Ideal for professionals eager to work on early-stage products and embrace continuous learning.
Key Responsibilities
- Model Development: Design, build, and optimize credit models for unsecured consumer credit products, collaborating with risk, business, and engineering teams.- Data Analysis: Analyze complex datasets, perform A/B testing, and extract actionable insights to support business strategies.- Innovation: Leverage advanced tools and new data sources to develop creative solutions and maintain cutting-edge credit systems.- Collaboration: Work closely with product managers, business analysts, and backend engineers to implement and monitor models and decisioning processes.- Leadership: Mentor data scientists and guide the development of analytical capabilities.- Strategic Testing: Design experiments to evaluate customer behavior and validate hypotheses.
Minimum Qualifications
- Bachelor’s degree in a quantitative field (e.g., Data Science, Mathematics, Statistics, Computer Science, or Engineering).- 3+ years of experience developing scorecards for consumer credit products.- Proficiency in Python, SQL, and experience with AWS.- Strong communication skills to articulate analytical concepts effectively.- Proven ability to independently manage projects and collaborate across teams.
Preferred Qualifications
- Experience with machine learning and statistical modeling.- Familiarity with MLOps and data engineering processes.- Self-motivated with a passion for continuous learning and innovation.
About You
You’re a results-driven problem solver with a passion for turning challenges into opportunities. You thrive in a fast-paced environment, adapt to changing contexts, and are eager to build something from the ground up.
This version combines similar statements, removes redundancies, and maintains professionalism while keeping the description concise and impactful.
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