Data Scientist

About the Role An Engineering company is seeking a skilled Data Scientist to join its dynamic team in Mumbai. In this role, you will have the opportunity to significantly impact our B2C personal loan product by building and refining data models. Collaborating closely with a dedicated data science team, technology experts, and product leaders, you will contribute to data-driven decision-making within a thriving organization. This position offers career growth potential alongside a committed team focused on innovation in the financial technology space. Responsibilities

• Develop and maintain robust credit scorecards and predictive models for consumer lending.
• Extract and engineer features from raw bureau data to improve model accuracy.
• Design, implement, and validate advanced statistical models to assess portfolio risks.
• Generate and refine fraud detection signals to mitigate potential losses.
• Collaborate with cross-functional teams to ensure alignment on data-driven strategies.
• Analyze model performance and iterate on design to enhance effectiveness.
• Present findings and insights to stakeholders to facilitate informed decision-making.
• Stay updated on industry trends and incorporate best practices in data science methodologies. Requirements
• A minimum of 5 years of experience in data science or analytics is required.
• At least 3 years of experience in

digital lending or consumer credit is essential.

• Proven track record in building and deploying credit scorecards and associated models.
• Experience in parsing and engineering features from raw bureau files is a must.
• Skilled in developing models for fraud detection and non-starters.
• Knowledge of repeat-borrower policies and limit-management frameworks is desirable.
• Familiarity with shadow underwriting and champion–challenger models is advantageous.
• Proficiency in SQL, Python, and data analysis libraries such as pandas and sklearn. Nice to Have
• Experience with ensemble methods like LightGBM, XGBoost, and Random Forest.
• Understanding of statistical modeling techniques using Statsmodels.
• Background in working with alternative data sources, including SMS and device signals.
• Previous exposure to financial regulations relevant to lending practices.
• Experience in deploying machine learning models in a production environment.