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 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
Skills Required
- Bachelor's degree in Computer Science, Engineering, or related field
- 3+ years experience as a data scientist, machine learning engineer, data engineer, or closely related role with production ML models
- Proven track record writing production-level code with documentation, testing and peer review
- High proficiency in Python and related libraries/frameworks (Scikit-Learn, Pandas, Flask)
- Comprehensive knowledge of the ML lifecycle, including serving and monitoring for live and batch processing
- Experience with cloud providers and containerization
- Experience handling large, complex datasets and feature engineering
- Good verbal and written communication skills in English
- Ability to work in a fast-paced environment with changing requirements
- Experience with AWS
- Experience in credit risk modeling or fraud detection
- Hands-on experience with Databricks for ML workflows at scale
PayJoy Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about PayJoy and has not been reviewed or approved by PayJoy.
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Fair & Transparent Compensation — Pay is considered market‑aligned for senior U.S. technical roles, and public salary bands help candidates benchmark and align expectations. Feedback suggests this transparency supports confidence that offers are competitive for role and location.
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Healthcare Strength — Company‑paid basic medical, dental, vision, life, and disability coverage is emphasized as a standout element versus many startups. Feedback suggests this reduces out‑of‑pocket burden and strengthens the core benefits foundation.
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Parental & Family Support — Paid parental leave and flexible hours are highlighted alongside dedicated time off. Feedback suggests these family‑oriented policies enhance the perceived completeness of the package.
PayJoy Insights
What We Do
PayJoy's mission is to deliver access to credit to the next billion people in emerging markets worldwide. Our unique mobile security technology gives customers the ability to afford their first smartphone on credit, using the phone itself as collateral, and then provides further access to credit to help weather life's unexpected financial surprises and climb the ladder of economic well-being. Founded in 2015, today PayJoy has reached millions of customers in a dozen countries around the globe, including Mexico, Brazil, Colombia, India, Kenya, and South Africa, and is on a strong growth path with support from major industry partners to bring credit to the next billion emerging consumers.
