Senior Data Scientist - Credit Risk Modeler - #4

Team: Operations

Location: Remote

Commitment: Remote Full Time

Workplace Type: remote

🚀 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).