Job description

About VARTIS Platforms (Formerly known as LenDenClub Techserve Pvt. Ltd.)

At VARTIS Platforms, we are a corporate group on a mission to democratize credit access and empower millions of people through cutting-edge technology. We focus on building trust and convenience, leveraging AI to drive the information revolution for smarter risk management and operational efficiency. VARTIS Platforms is profitable, rapidly scaling, and industry-recognised. Our impact has been acknowledged with top honours, including being awarded as the Best Fintech Provider by Financial Express, a testament to our product excellence, customer experience, and leadership in regulated financial technology.

Our portfolio includes:

LenDenClub: An RBI-licensed P2P marketplace that connects borrowers with lenders in a seamless digital ecosystem.

InstaMoney: Instant digital loans that enable quick, secure, and accessible financial support.

NiveshGold: India’s next-gen digital vault for wealth preservation, starting with 100% pure, insured 24K digital gold.

UdharSaathi: A structured working capital platform built exclusively for QR active kirana stores, pharmacies, and small retailers.

Role Overview

We are looking for two experienced Data Scientist / ML Engineer (4+ years) to build intelligent decisioning and AI systems for complex financial use cases. The role involves developing predictive models, next-best-action systems and agentic AI capabilities that translate customer and behavioural data into actionable decisions.

You will own ML solutions end-to-end — from problem formulation and feature engineering to modelling, validation, deployment and monitoring — while working closely with the AI Product Lead and engineering teams.

Must-Have Skills

Strong proficiency in Python and SQL, with hands-on experience in Scikit-learn, XGBoost/LightGBM and TensorFlow/PyTorch.
Strong experience building predictive and decisioning models using behavioural, transactional, bureau and other customer data.
Experience with models/use cases such as Next Best Action, propensity modelling, payment/bounce prediction, customer segmentation, risk or response prediction.
Hands-on experience taking ML models to production, including validation, monitoring, performance analysis and retraining.
Strong statistical and modelling discipline, with the ability to identify data leakage, instability, bias and genuine predictive lift.
Ability to translate ambiguous business problems into end-to-end ML/AI solutions and communicate trade-offs clearly to product, business and engineering teams.
Working understanding of LLMs and Agentic AI, with the ability to integrate predictive ML models into larger AI/agentic decisioning workflows.

Preferred Skills

Experience with Next Best Action / recommendation / optimization / propensity modelling.
Exposure to LLMs, agentic workflows, RAG or AI-driven decision systems.
Working knowledge of AWS/GCP for ML and data workloads.
Experience with MLOps — Git/DVC, experiment tracking, CI/CD, model monitoring and automated retraining.
Sound understanding of data architecture, feature pipelines and low-latency model serving.

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