Location
In office (Bangkok)
Seniority
Intern
Employment
Internship
Confirmed on the employer's own hiring board on Nov 3, 2026. First seen by Alion on Oct 19, 2026.
Overview
Company
Impact
Profile match
ABACUS digital is a financial technology enterprise specializing in AI-driven digital lending, alternative credit assessment, and inclusive financial services. Headquartered in Bangkok, Thailand, the company operates digital credit platforms - including its flagship MoneyThunder mobile application - powered by proprietary real-time machine learning risk models. By automating loan origination, credit evaluation, and capital disbursement using non-traditional data points, the enterprise enables unbanked consumers, micro-entrepreneurs, and small businesses across Southeast Asia to access formal financing.
Backed by Open Space Capital
What You Will Do
- Assist in building and evaluating machine learning or statistical models, from problem formulation to implementation.
- Conduct exploratory data analysis to identify predictive signals and improve model performance.
- Support research projects and help translate business problems into data-driven solutions.
- Collaborate with data scientists, engineers, and business teams to design experiments and modeling approaches.
Who We Are Looking For
- Currently pursuing a Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or related quantitative fields.
- Strong foundation in probability, statistics, and machine learning.
- Programming skills in Python and familiarity with SQL.
- Understanding of the end-to-end model development process is a plus.
- Good communication skills and ability to work with cross-functional teams.
What You’ll Gain
- Hands-on experience in developing and deploying machine learning models in the fintech industry.
- Mentorship from experienced data scientists and exposure to production-level AI/ML workflows.
- Opportunity to contribute to real projects that impact customer experience and financial access.
- Practical learning in experimentation, data pipelines, and scalable modeling practices.
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