Data Scientist

Requirements

• 2–5 years of experience in credit risk analytics, lending analytics, or fintech data science.
• Master’s degree in a quantitative field such as Statistics, Computer Science, Economics, Applied Mathematics, or related discipline.
• Hands-on experience in statistical modeling, machine learning, and predictive analytics.
• Strong ability to work with messy, real-world datasets (incomplete, noisy, biased) and large-scale data processing.
• Experience in small business lending, fintech, or alternative credit ecosystems.
• Familiarity with model governance, validation frameworks, and explainability techniques (e.g., SHAP).
• Exposure to cloud environments (AWS) and modern data engineering workflows.
• SQL & Snowflake for data extraction, transformation, and large-scale querying.
• Python for modeling, automation, and data analysis (Pandas, NumPy, Scikit learn, etc.).
• Tableau (or similar BI tools) for data visualization and stakeholder reporting.
• Understanding of ML lifecycle (training, validation, deployment, monitoring).
• Exposure to API integration and production-level model deployment.