Senior Data Scientist (AI/ML) in India, India - Jobeax
Vacancy description
Senior Data Scientist (AI/ML) in India, India
Cashera
India, India
Senior Data Scientist (AI/ML) in India, India is listed on Jobeax. Browse 30,000+ vacancies available.
About Cashera Cashera is a fast-growing fintech and lending company that provides funding solutions to businesses across multiple industries in the United States and UK. Cashera combines technology, data-driven decision-making, and industry expertise to deliver fast, flexible financing options that traditional lenders often cannot provide.
As a Senior Data Scientist on the Credit & Fraud Risk Team, you will take ownership of the end-to-end lifecycle of predictive machine learning models. You'll play a foundational role in building systems that evaluate borrower creditworthiness, mitigate operational loss, and intercept real-time fraud vectors. This is a business-critical position reporting directly to Risk and Data Science Leadership, collaborating closely with Engineering, Product, Collections, and Finance teams.
Build, evaluate, and scale predictive models using XGBoost, LightGBM, and Deep Learning for underwriting, credit line assignment, and collections. Familiarity with gen-AI techniques for feature creation and transaction tagging on bank transactional data is a plus.
Credit Risk Modeling: Build credit risk models for sub-prime and near-prime customers in a fintech environment with short model build cycles.
Fraud & Risk Defense: Design and deploy real-time decisioning rules and ML systems to detect synthetic fraud, digital identity theft, account takeovers, and transactional fraud.
Feature Engineering: Mine complex, large-scale, and alternative data streams (bank transactional data, logs, credit bureau reports, structured/unstructured digital signals) to extract predictive signals.
Partner with Credit Risk Strategists to translate model outputs into actionable credit limits, cutoff thresholds, and loss-forecasting simulations.
Platform Architecture: Collaborate with Data Platform Engineers to build reliable pipelines and high-throughput feature extraction.
Raise the technical bar through peer reviews, reusable tooling, and pro-active communication to risk leadership and partners.
in a quantitative field (Statistics, Mathematics, Computer Science, Economics, Data Science) or equivalent practical experience.
Experience: 2–4 years of Data Science experience, ideally including 3+ years focused on Credit Risk, Fraud Analytics, Lending, or Fintech.
Production-grade fluency in Python and advanced, highly analytical SQL.
Modern Stack Experience: Hands-on experience scaling data workflows over frameworks like Snowflake, Databricks, Spark, dbt, or similar platforms.
ROC-AUC curves, model drift scenarios) to non-technical stakeholders.
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