Data Scientist (Remote)
Company: Cinch
Location: Jakarta, Jakarta (Remote)
Type: Full-time
Remote: Yes
Posted: 2026-11-13
About this role
*Reimagine access. Reimagine ownership. Build the future with Cinch.*
Cinch is Southeast Asia's leading circular tech subscription company, redefining how people and businesses access technology. Instead of adding to the growing e-waste crisis, we enable affordable, sustainable access to devices through subscriptions that maximize reuse and extend their lifecycle.
We operate across B2C, B2B, and B2B2C channels, including major partnerships with global leaders like Samsung. Our model sits at the intersection of powerful mega trends—sustainability, embedded finance, and the shift from ownership to access—creating a massive opportunity to reshape the future of tech consumption.
At Cinch, we are builders with high standards, a bias for action, and a shared mission to create a generational company that outlasts us. Freshly funded by leading institutional investors and growing rapidly, we're creating an environment where ambitious people can do the best work of their careers—and where the work has real, lasting impact.
The Role
We are looking for a Data Scientist to build the credit models that decide who Cinch lends to, on what terms, and how we collect. Every subscription at Cinch is a customer relationship and a credit exposure on a physical device, so your models directly shape growth, losses and asset returns.
You will own credit and risk models end to end: from data exploration and feature design through validation, deployment and monitoring. You will work with SQL and Python, partner with Data, Product and Engineering teams, and turn model outputs into decisions the business can act on. You are expected to be AI-literate and comfortable working with Claude. We expect responsible AI use, and the correctness of all work submitted remains the author's own responsibility.
Responsibilities
Risk and credit modelling
- Build, validate and maintain credit scoring models for new and repeat subscribers - A Score and B Score models.
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