Key Responsibilities of the Data Scientist

Build real-time business performance dashboards for risk monitoring.
Design risk assessment scorecards for all lending stages (pre-acquisition to recoveries) using explainable ML techniques.
Develop proprietary variables from mainstream (credit bureau, application data) and alternative sources (social media, transaction patterns).
Implement supervised/unsupervised learning models combining structured and unstructured data.
Convert raw data (bureau, transactional, social media) into actionable features via techniques like dimensionality reduction, interaction terms, and temporal aggregation.
Maintain a continuously-updating Business Rule Engine that codifies lending policies using Python.
Optimize Python-based feature pipelines for GPU acceleration (CUDA/Numba) and Spark/Dask distributed processing.
Establish data handling protocols for extraction, cleaning, and pipeline maintenance.

Collection Insights & Analytics

0 to 2 years of experience with Power BI and SQL as a data analyst or similar role.
Strong analytical mindset with attention to data accuracy and problem solving, and an ability to work independently.
Strong background in writing advanced SQL queries (joins, filters, window functions, etc.).
Experienced at building Power BI data models and manage relationships between multiple tables.
Comfortable using DAX for building complex measures, calculated columns, Parameters etc.
Ability to troubleshoot and debug issues when reports or data pipelines break.
Effective communication skills to translate business needs into analytical solutions.
Experienced in working with large datasets from different sources.
Basic knowledge of Python for data wrangling (numpy, pandas).