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Global brand, popular Data Scientist title, Bangalore location, and broad Python/ML skillset create high competition.
Core Python and ML skills are transferable, but insurance pricing knowledge favors industry experience.
Explicit 2–4 years and required Python/ML production skills imply medium strictness in shortlisting.
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Develop and implement machine learning and statistical models (GLM, regression, classification, clustering) to support pricing and risk assessment in Property & Casualty insurance.
Perform exploratory data analysis, data validation, and preprocessing on large complex datasets to generate business insights.
Collaborate with global business stakeholders to deliver analytics solutions and present findings, contributing to knowledge-sharing and mentoring within the data science team.
2-4 years professional experience in Python for data analysis, modeling, and production development.
Strong proficiency in Python (including OOP, design patterns) and experience with machine learning libraries such as scikit-learn and XGBoost.
Familiarity with Agile environments, Jira, and production-quality code development practices.
Work Experience Required: 2-4 years relevant data science experience in analytics/modeling.
Experienced in handling insurance data, especially Property & Casualty product lines and related pricing models (not mandatory but preferred).
Ability to work with both technical and non-technical stakeholders globally, managing deliverables and timelines.
Comfortable working in an Agile setup with emphasis on quality production code and knowledge sharing within teams.