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Tier-1 brand, metro Bangalore location, and mid-level Data Scientist title (4-6 years) drive high competition.
Insurance pricing and actuarial familiarity preferred, moderately reducing cross-industry transferability.
Explicit 4-6 years requirement plus mandatory ML, Python, GLM and Azure/Databricks skills increases strictness.
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Develop, build, and deploy machine learning and GLM pricing models to support property and casualty insurance products, focusing on driving business value through predictive analytics.
Collaborate with global business partners to manage deliverables, ensure timely model implementation, and communicate results to technical and non-technical stakeholders.
Research and apply statistical and mathematical methods to solve complex business problems, create adaptable modeling frameworks, and synthesize data insights to recommend improvements.
4-6 years of experience in building machine learning/statistical models.
Advanced degree preferred in Statistics, Mathematics, Analytics, Computer Science, Engineering, or related fields; actuarial exam passes considered advantageous.
Strong proficiency in Python/PySpark, including machine learning libraries (scikit-learn, H2O, MLlib, etc.) and knowledge of ML concepts (GLM, Random Forest, XGBoost, SVM, etc.).
Experience in developing/maintaining models supporting property and casualty insurance products; knowledge of building data pipelines in Azure/Databricks.
Experienced with pricing modeling in property and casualty insurance and comfortable collaborating across actuarial, IT, product, and analytics teams.
Skilled in managing complex analytics projects end-to-end including requirements gathering, model development, validation, and stakeholder communication.
Pragmatic problem solver with a strong quantitative/statistical background and ability to translate business needs into robust, scalable analytics solutions.