Job Description

About LenDenClub

LenDenClub is India's largest Peer-to-Peer (P2P) Lending platform, owned and operated by Innofin Solutions Pvt.Ltd., an RBI-registered (NBFC-P2P). It provides an opportunity to lenders looking for high interest rates by connecting them with creditworthy borrowers looking for short-term loans. LenDenClub has become a go-to platform to earn returns, lenders have historically earned an XIRR 11.28% p.a. The platform has an active user base of over 2 Cr+ users and loan disbursal of over ?15,000 crores.
LenDenClub also owns InstaMoney which is one of India's leading D2C short term personal loan apps with over 30M downloads and annual growth rate of multiple X.

Why work at LenDenClub

At LenDenClub, we take pride in being the first Peer-to-Peer lending platform to offer ESOP liquidity, and this achievement has allowed us to set a new benchmark. The progressive approach towards employee benefits has been acknowledged and appreciated and as a result, LenDenClub has been certified as a Great Place to Work successively for three years by the Great Place to Work Institute.

As a LenDenite, you will be a part of an enthusiastic and passionate group of individuals who own and love what they do. At LenDenClub, we believe in creating leaders, and you will get to work with complete freedom to chase your ultimate career goal without any inhibitions. We remain committed to continuing our journey of innovation and fostering a positive work environment for our team.

Profile Summary

We are seeking a highly experienced Lead Advanced Analytics professional with 10+ years of expertise in developing cutting-edge credit underwriting models using modern machine learning techniques and alternative data sources. This role will drive innovation in our lending decision-making processes and lead a team of analytics professionals in a dynamic fintech environment.

Job Responsibilities

Advanced Model Development & Innovation

Design sophisticated credit underwriting models using modern ML algorithms (XGBoost, Random Forest, Neural Networks, Ensemble methods) Build predictive models leveraging alternative data sources including digital footprints, transactional data, behavioral analytics Implement real-time scoring engines and automated decision-making systems Develop champion-challenger frameworks for continuous model optimization

Alternative Data Integration & Feature Engineering

Identify, evaluate, and integrate alternative data sources to enhance model performance Build models using psychometric data, device analytics, social network analysis, and open banking data Work with unstructured data sources including text, images, and behavioral patterns Implement automated feature selection and dimensionality reduction techniques

Machine Learning & AI Implementation

Apply state-of-the-art ML techniques including deep learning, gradient boosting, and ensemble methods Implement explainable AI solutions to ensure model interpretability Develop automated machine learning pipelines for model training, validation, and deployment Explore and leverage generative AI technologies for innovative underwriting solutions

Team Leadership & Strategic Direction

Lead and mentor a team of data scientists and analytics professionals Take end-to-end ownership of analytics projects from conception to production deployment Collaborate with cross-functional teams including product, engineering, and business stakeholders Drive analytics strategy and roadmap aligned with business objectives

Model Deployment & Operations

Implement MLOps practices for streamlined model deployment, monitoring, and maintenance Collaborate with data engineering teams to ensure robust data pipelines and infrastructure Develop automated model monitoring systems and performance tracking dashboards Build A/B testing frameworks for model performance evaluation

Business Impact & Insights

Analyze large and complex datasets to extract meaningful insights and trends Create clear and comprehensive reports, dashboards, and visualizations for stakeholders Present findings and recommendations to both technical and non-technical audiences

Required Qualifications

Experience & Domain Expertise

Minimum 10 years in advanced analytics with focus on credit underwriting in BFSI domain Proven track record in fintech/digital lending environments developing credit scorecards using modern ML Extensive experience with alternative data integration and non-traditional credit scoring methods Leadership experience managing teams of data scientists or analytics professionals

Technical Skills & Programming

Advanced proficiency in Python, R, SQL, and statistical analysis tools Expertise in scikit-learn, XGBoost, TensorFlow, PyTorch, and advanced ML libraries Experience with big data technologies (Hadoop, Spark) and cloud platforms (AWS, Azure, GCP) Proficiency in data visualization tools like Tableau, Power BI, or similar platforms Knowledge of MLOps tools (MLflow, Docker, Kubernetes) for model operations

Advanced Analytics Expertise

Deep expertise in statistical modeling, machine learning algorithms, and predictive analytics Strong background in feature engineering, model selection, and hyperparameter optimization Experience with NLP techniques for processing unstructured data and text analytics Proficiency in experimental design, A/B testing, and causal inference techniques

Alternative Data & Innovation

Proven experience with alternative data sources (digital, behavioral, psychometric, social media) Knowledge of open banking, API integrations, and real-time data processing Experience with computer vision, NLP, and unstructured data analysis Understanding of data privacy, ethical AI, and responsible lending practices

Leadership & Communication

Excellent leadership skills with ability to inspire and develop technical teams Strong stakeholder management and cross-functional collaboration abilities Exceptional communication skills for presenting complex concepts to business audiences Self-driven and comfortable working in fast-paced startup environments

Preferred Qualifications

Advanced degree (B.Tech/M.Tech) in Engineering, Computer Science, Mathematics, or Statistics Experience with embedded finance, digital-first lending platforms, or API-based financial services Knowledge of microservices architecture and modern software development practices * Understanding of various lending products and fintech ecosystem dynamics