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Tier-1 bank, mid-level ML/data scientist in Bangalore with common skills drives high applicant competition.
Strong banking risk and regulatory modeling context makes domain-specific experience highly important.
Mandatory 4+ years, specific ML/Big Data stack, and regulatory model experience increases shortlisting strictness.
Lead creation, implementation, and documentation of complex quantitative models for market, credit, and operational risks.
Forecast losses and compute capital requirements using advanced statistical and machine learning techniques, providing actionable insights.
Partner with regulators, auditors, and business stakeholders; manage end-to-end model lifecycle including development, validation, deployment, and governance.
Master's degree or higher in quantitative disciplines such as mathematics, statistics, engineering, physics, economics, computer science, or related fields.
4+ years of quantitative analytics or equivalent experience required.
Hands-on expertise with Python, PySpark, SQL, and machine learning/statistical modeling techniques including regression, random forest, XGBoost, GBM, SVM, and deep learning frameworks (e.g., TensorFlow, Keras).
Experience with big data platforms and tools such as Spark, Hadoop, H2O, Teradata, or Google Cloud Platform.
Experienced in operating at the intersection of business and technical teams, with ability to consult and communicate complex analytic results to partner-facing stakeholders.
Proficient in building, validating, and deploying scalable predictive models in a regulated banking environment, with strong knowledge of model governance.
Comfortable managing multiple data science projects independently and mentoring junior data scientists, utilizing advanced analytics and AI across business verticals.