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Medium — mid-level, metro fintech ML role with generalist skills but requires lending domain expertise.
High — requires deep lending economics, regulatory knowledge, and fintech-specific modeling experience.
High — explicit years, mandatory fintech credit domain expertise, and specific ML/production tech stack.
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Own end-to-end development and deployment of machine learning models across the borrower lifecycle including acquisition, credit risk underwriting, and collections.
Translate business requirements into mathematical frameworks and lead full ML lifecycle: data pipeline creation, model training, production deployment.
Collaborate with business leaders, act as a bridge between technical teams and stakeholders; mentor junior data scientists and enforce code quality.
5-9 years of hands-on experience in Data Science, preferably in FinTech, consumer lending, or banking.
Expert proficiency in Python (Pandas, Scikit-learn, NumPy) and SQL for large-scale data manipulation.
Experience with machine learning techniques including ensemble methods (XGBoost, LightGBM) and statistical models like Logistic Regression.
Familiarity with big data frameworks (e.g., PySpark), cloud platforms (AWS, GCP, Azure), and ML deployment tools (Git, Airflow, MLflow).
Experienced practitioner with deep domain knowledge of lending economics and regulatory environment within financial services.
Demonstrated ability to develop models for multiple lending-related areas: marketing/growth, credit risk scorecards, or collections/recovery.
Technical leader comfortable bridging business and technical teams, with a track record of mentoring and elevating data science teams in production-grade ML systems.