PayJoy is a mission-first financial service provider dedicated to helping under-served customers in emerging markets to achieve financial stability and success. We lend through our patented technology that turns a smartphone into digital collateral, and our cutting-edge machine learning, data science, and anti-fraud AI allow us to offer the lowest cost and qualify the most customers in the industry. As of 2025 we have brought billions of dollars in credit to 15 million customers, doubling in the last two years while remaining strongly profitable and sustainable for the long term.
This role
As a Machine Learning Engineer, you will be responsible for developing, optimizing and deploying ML models that power our fraud detection, credit risk and other applications like cross-sell, churn and collections.
You will work closely with risk, fraud, engineering, product and business stakeholders across diverse markets to drive the design, implementation and scaling of ML models. Your role will also involve ensuring that we are continuously improving the quality and performance of our models by gathering and integrating new data sources that enhance our predictive capabilities.
You will own the whole lifecycle of our ML models, from the feature generation to the model rollout (design, development, deployment and monitoring).
You will be part of a data science team on a mission to improve access to credit and technology in emerging markets with the opportunity of creating a big and real positive impact to our millions of users across the countries we operate in.
Responsibilities
- Collaborate with global teams including Risk, Fraud, Engineering and Product to deliver world-class data science products to international markets in Latam, South Africa and APAC.
- Design, build, and deploy machine learning models for a variety of use cases, including fraud detection, credit risk modeling, customer segmentation, collections, and churn.
- Ensure our delivered ML models are production-ready, optimized for scale and continuously improved based on feedback from our stakeholders and performance on production.
- Handle large, complex datasets to clean, preprocess and extract relevant features to improve model accuracy and performance.
- Write production-level code with documentation, testing and peer review.
- Work with a data-driven mindset and understand the critical importance of handling data properly and safely.
- Lead the testing, cost-benefit analysis and integration of new data sources to improve the accuracy and robustness of our ML models.
- Work closely with our ML Platform and Tooling team to design and implement scalable feature generation and extraction pipelines and model deployment/monitoring processes.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field.
- 3+ years of experience as a data scientist, machine learning engineer, data engineer or a closely related position with a proven track record of writing production-level code and developing and maintaining ML models in production.
- High proficiency in Python and a strong understanding of its related libraries and frameworks (e.g., Scikit-Learn, Pandas, Flask, etc).
- Comprehensive knowledge of ML life cycle: from data extraction and feature engineering to model serving and monitoring for live and batch processing.
- Demonstrated experience with cloud providers (AWS preferred) and related services like containerization (e.g., Docker).
- Experience in credit risk modeling, fraud detection or other applications of machine learning in the financial market is a big plus.
- Good verbal and written communication skills in English.
- Ability to work in a fast paced environment with constant requirement changes.
Benefits
- *Local benefits will depend of the country of hiring*
- 100% Company-funded Health and Dental insurance for employees and immediate family members.
- Phone finance, Headphone, home office equipment and fitness perks.
- $2,000 USD annual Co-working Travel perk.
- $2,000 USD annual Professional Development perk.
PayJoy Principles
Finance for the next billion * Ownership * Break Through Walls * Live Communication * Transparency & Directness * Focus on Scale * Work-Life Balance * Embrace Diversity * Speed * Active Listening
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