Machine Learning Engineer - Data Science

About PayJoy   PayJoy, a Public Benefit Corporation, is a mission-first credit provider dedicated to helping under-served customers in emerging markets to achieve financial stability and success.  Our patented technology for secured credit provides an on-ramp for new customers to enter the credit system.  Through PayJoy’s point-of-sale financing and card offerings, customers gain access to a modern quality of life.  PayJoy’s credit also allows our customers to seize opportunities as micro-entrepreneurs, and acts as insurance for tough times. Through our cutting-edge machine learning, data science, and anti-fraud AI, we have served over 18 million customers as of 2025 while achieving solid profitability for sustainable growth.   This role 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 in 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 lifecycle: 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. • Hands-on experience with Databricks for developing, deploying and monitoring machine learning workflows at scale is also a plus. • Good verbal and written communication skills in English • Ability to work in a fast paced environment with constant requirement changes. Benefits • 100% Company-funded Health and dental and vision discount plan for employees and immediate family members. • Life insurance. • Phone finance, Headphone, home office equipment and wellness perks. • 30 days of Christmas bonus • 20 days paid Vacation • 50% Vacation premium • 13% Saving funds • $2,000 MXN monthly grocery coupons • $2,000 USD annual Co-working Travel perk • $2,000 USD annual Professional Development perk • Catered lunches PayJoy is proud to be an Equal Employment Opportunity employer and we welcome and encourage people of all backgrounds. We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.   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