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Senior Data Scientist - Credit Risk Modeler - Databricks

MUTT DATA

Role overview

Qualifications

  • Proven experience in credit risk / scoring models and supervised machine learning
  • Python (scikit-learn, XGBoost), statistics, model validation, and MLflow
  • Understanding of risk metrics (PD, expected loss, exposure)

Responsibilities

  • Take ownership of the existing model (tree ensembles / gradient boosting), retrain it, and incorporate new features (e.g., digital payments, CISP)
  • Evaluate performance (AUC-ROC, F1, probability calibration) and segment risk levels A–F aligned with credit standards
  • Calculate dynamic credit lines and expected loss (risk exposure), integrating score, potential, and sales history
  • Package the model under the MFL framework (PyFunc, model_card, tests) for productionization

Key facts

Hard skills

Other skills

  • Collaboration

About the company

MUTT DATA logo

MUTT DATA

Artificial Intelligence & Machine Learning Services

Mutt Data is a technology company that helps startups and big companies build and implement Machine Learning solutions that drive real business results. Whether you work in finance, insurance, advertising, telcos, on-demand services or e-commerce, our solutions will help you get ahead of your competition with the latest technologies, techniques and best practices We Are Astronomer & Amazon Consulting Partners. We Are #DataNerds.

Company details

Company typeScaleup
IndustryArtificial Intelligence & Machine Learning Services
Company size51 - 200

Links

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Job description

🚀 Join Our Remote Data Products & Machine Learning Startup! 🚀

At Muttdata, we build innovative Data Products and Machine Learning solutions that help companies solve complex business challenges. As a fast-growing, remote-first startup, we're passionate about technology, collaboration, and continuous learning.

This opportunity is with a leading multinational beverage company based in Mexico City.

We are looking for a Senior Data Scientist  - Credit Risk Modeler to join our team 🐶🚀. You'll inherit, maintain, and evolve our Credit Score model (Hit / No Hit), applying credit-risk modeling expertise to segment the portfolio by probability of default and enable dynamic credit lines.

This role works closely with data and platform teams, taking ownership of a live financial model and evolving it responsibly. Strong statistical rigor, business understanding of credit risk, and ownership are essential to succeed in this fast-paced, collaborative environment.


🚀 What We Do
  • Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.
  • Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
  • Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
  • Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
  • Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
  • Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.

    • 🌟 Our Partnerships
  • Amazon Web Services
  • Astronomer
  • Databricks

    • 🌟 Our Values
  • 📊 We are Data Nerds
  • 🤗 We are Open Team Players
  • 🚀 We Take Ownership
  • 🌟 We Have a Positive Mindset
    •   🔍 Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on! 🚀
    Responsibilities 🤓
  • Take ownership of the existing model (tree ensembles / gradient boosting), retrain it, and incorporate new features (e.g., digital payments, CISP).
  • Evaluate performance (AUC-ROC, F1, probability calibration) and segment risk levels A–F aligned with credit standards.
  • Calculate dynamic credit lines and expected loss (risk exposure), integrating score, potential, and sales history.
  • Package the model under the MFL framework (PyFunc, model_card, tests) for productionization.

    • Required Skills 🚀
  • Proven experience in credit risk / scoring models and supervised machine learning.
  • Python (scikit-learn, XGBoost), statistics, model validation, and MLflow.
  • Understanding of risk metrics (PD, expected loss, exposure).

    • Nice to Have Skills 😉
  • Experience in financial services, credit bureaus, or commercial credit portfolios.
  • Experience developing AI agents / agentic infrastructure (e.g. Mosaic AI Agent Framework, agent orchestration, MCP).
    •

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    MR

    Marcus Rivera

    Chief Revenue Officer

    m.rivera@company.com
    linkedin.com/in/marcusrivera
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