Lead Data Scientists & ML (Remote)

Opportunity to work as a Data Scientists in a Leading Mexican Fintech on the path to becoming an universal bank.

Location: Mexico City, Flexible (with relocation options). Details discussed individually.

About Us



We are an ambitious and rapidly growing fintech startup based in Mexico, driven by the mission to redefine banking in Latin America. As we scale to become a universal bank, we are looking for forward-thinking professionals ready to build innovative solutions from the ground up.

What We Offer

$6,000 to $6,500 USD NET Salary

Flexible work locations, with relocation options available (Remote job)

The opportunity to shape the companys ML and risk strategy from the ground up, with direct impact on financial inclusion in Latin America.

A competitive compensation package, aligned with your experience and impact.

A mission-driven, collaborative team building the future of finance in Latin America.



What Were Looking For

Bachelors or Masters degree in Data Science, Computer Science, Statistics, Applied

Mathematics, or related field. (Titulado)

English: Advanced (B1 or B2)

Exp. in applied machine learning, with a focus on credit risk, consumer lending, or financial services  (+ 5 years)

Proven expertise in Python and SQL (+ 5 years)

Strong grasp of ML frameworks (e.g., scikit-learn,XGBoost, TensorFlow/PyTorch(nice to have). (+ 5 years)

Experience deploying and maintaining ML models in production (+ 5 years)

Familiarity with credit bureau data, behavioral analytics, and alternative data sources.

Experience with BI/visualization tools (e.g., Tableau, Power BI, Looker) (+ 5 years)

What Youll Do

As Lead Data Scientist in Risk, you will own and shape the companys machine learning landscape with a focus on credit risk and portfolio health. You will drive the design, development, and deployment of advanced ML solutions to improve credit decisioning and risk strategies across the entire customer lifecycle. Your main responsibilities will include:

ML & Scoring Ownership: Design, build, and continuously improve credit scoring

models using credit bureau, behavioral, and alternative data sources.

Model Innovation: Leverage advanced ML/AI techniques (e.g., gradient boosting,

neural networks, feature engineering from alternative data) to optimize risk assessment and enable financial inclusion.

End-to-End Deployment: Own the ML lifecycle from experimentation to production, ensuring scalable, robust, and explainable models in partnership with engineering teams.

Risk Intelligence: Set up advanced monitoring, model validation, and performance

tracking systems to ensure predictive stability and compliance.

Product Partnership: Collaborate closely with product, operations, and risk analysis teams to integrate ML-driven solutions into decision-making across lending, collections, and new product launches.

Thought Leadership: Act as the subject-matter expert in data science for risk,

mentoring team members and setting best practices for model governance, fairness, and interpretability.