Data Science Executive

Role Summary

Own the vision, strategy, and execution of TapLend's Data Science function by building and scaling a high-performing team, driving AI and analytics initiatives, and delivering scalable machine learning solutions across underwriting, credit risk, fraud detection, collections, and lending operations.

Key Responsibilities

• Lead, mentor, and scale the Data Science and Analytics team.
• Define and execute the AI and Data Science roadmap aligned with business objectives.
• Design, build and deploy machine learning models for credit risk, fraud detection, affordability assessment and customer behavior.
• Develop AI-powered solutions for underwriting, collections, customer support and operational automation.
• Own the end-to-end machine learning lifecycle, including feature engineering, model development, deployment, monitoring and continuous improvement.
• Partner closely with Product, Risk, Engineering and Business teams to deliver measurable business impact.
• Present strategic insights, model performance and recommendations to senior leadership.
• Drive innovation by evaluating and adopting emerging AI and Generative AI technologies.
• Establish best practices for experimentation, model governance and AI development.
• Hire, mentor and retain top Data Science talent. Technical Skills
• Python, SQL, Pandas, NumPy
• Machine Learning: XGBoost, LightGBM, CatBoost, Scikit-learn
• Credit Risk Analytics, Scorecards, PD/LGD concepts, Bureau Analytics
• Fraud Detection, Portfolio Analytics, Affordability & Early Warning Models
• ClickHouse, PostgreSQL, MySQL, Spark
• Power BI, Tableau or Grafana Good to Have Skills
• Experience with LLMs, AI Agents, RAG, LangChain, LangGraph, LlamaIndex or similar frameworks.
• Knowledge of Credit Risk Modelling, Credit Bureau data and lending analytics.
• Exposure to MLOps, Docker, Cloud platforms (AWS/Azure/GCP) and production ML systems.

Qualifications

• Bachelor's/Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related field.
• 0-2 years of Data Science or Machine Learning experience.
• At least 1 years of experience leading and mentoring high-performing Data Science or Machine Learning teams.
• Prior experience in NBFCs, Banking, Lending or FinTech is strongly preferred.