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Popular Data Scientist title, metro Gurgaon location, and broad ML skillset create high candidate competition.
Fraud-focused ML in FinTech requires domain-specific expertise, making background fit highly sensitive.
Explicit 5-8 years requirement plus mandatory production ML skills and specific tech stack increases shortlisting strictness.
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Own end-to-end lifecycle of machine learning models focused on fraud detection, risk scoring, and credit underwriting for fintech and banking.
Analyze large-scale behavioral and transactional data to detect fraud patterns and emerging threats.
Collaborate with engineering and product teams to deploy, monitor, and iteratively improve production ML models at scale.
5+ years of data science experience; exposure to FinTech or financial services preferred.
Bachelor's degree or higher in Computer Science, Statistics, Applied Mathematics, or related field.
Proficiency in Python and SQL and experience with ML frameworks (scikit-learn, XGBoost, PyTorch, or TensorFlow).
Experience deploying models to production with capabilities to monitor and iterate on them.
Strong background in statistical modeling, classical ML techniques, and experimental design for fraud/risk domains.
Experienced in operationalizing machine learning models including monitoring and retraining in production environments.
Ability to translate complex technical insights into clear communication for both technical and business stakeholders.