Muttdata

Senior Data Scientist - Credit Risk Modeler - Databricks

Muttdata

IT Services and IT Consulting · 51-200 employees

20 h ago
Remote data-scientist Senior (5-10 yrs) Full-time
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About the role

The Senior Data Scientist will take ownership of the existing credit score model, retraining it and incorporating new features to improve performance. They will also evaluate risk levels, calculate dynamic credit lines, and ensure the model is properly packaged for production.

What they look for

Credit risk modeling Machine learning Python Scikit-learn XGBoost Statistics Model validation MLflow Data products Financial modeling Probability of default Risk metrics Databricks PyFunc Model card

Requirements

Candidates must have proven experience in credit risk modeling and supervised machine learning using Python tools like scikit-learn and XGBoost. A strong understanding of risk metrics such as PD and expected loss is essential for this role.

Full 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.

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🚀 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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