Lead Data Scientists & Ml (Remote)

Thebricksoluciones; A distancia; MXN 400,000 - 600,000; Jornada completa

Descripción de la vacante

A leading Mexican fintech is seeking a Data Scientist to drive machine learning solutions focused on credit risk. The role involves designing and deploying advanced models that enhance financial decision-making and promote financial inclusion in Latin America. Candidates should have significant experience in applied machine learning, Python, and SQL, along with a strong educational background in Data Science or related fields. Competitive salary ranging from $6,000 to $6,500 USD NET, with flexible work options available.

Servicios

Competitive salary

Formación

• 5+ years of experience in machine learning focused on credit risk or financial services.
• Strong experience in deploying and maintaining ML models in production.
• Familiarity with credit bureau data and behavioral analytics.

Responsabilidades

• Design and continuously improve credit scoring models.
• Drive deployment of advanced ML solutions for credit decisioning.
• Collaborate with product and operations teams for ML integration.

Conocimientos

Applied machine learning

Proficiency in Python

Experience with SQL

Advanced English (B1/B2)

Experience with ML frameworks

Experience with BI tools

Educación

Bachelors or Masters degree in Data Science or related field

Herramientas

Tableau

Power BI

TensorFlow

PyTorch

scikit-learn

XGBoost

Descripción del empleo

Opportunity to work as a Data Scientist 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 company's 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 We're Looking For

Bachelors or Masters degree in Data Science, Computer Science, Statistics, Applied Mathematics, or related field.

• English: Advanced (B1 or B2)
• Experience 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))
• 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 You'll Do

As Lead Data Scientist in Risk, you will own and shape the company's 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 analysts 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.