Role Overview /n We are seeking highly skilled Analytics & Data Science professionals (5–7 years of experience) with solid domain expertise in Insurance/Financial Services. The role focuses on developing data-driven solutions across Fraud Detection, Risk Prediction, and Underwriting Analytics. /n Key Responsibilities /n /n
Develop and deploy machine learning models for fraud detection, underwriting risk, and customer risk profiling.
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Build scalable data pipelines and feature engineering frameworks using Python and SQL.
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Design and implement end-to-end ML workflows including model training, validation, and deployment.
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Work with business stakeholders to translate business problems into analytical solutions.
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Perform exploratory data analysis (EDA), model monitoring, and performance tuning.
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Create impactful dashboards and reports using Power BI/Qlik Sense for decision-making.
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Collaborate with data engineering and IT teams for production-grade model deployment.
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5–7 years of experience in Analytics & Data Science roles.
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Domain experience in Insurance or Financial Services (Fraud, Risk, Underwriting preferred).
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Solid programming skills in Python and SQL.
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Hands-on experience with machine learning techniques and model lifecycle management.
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Experience with visualization tools such as Power BI/Qlik Sense.
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Understanding of ML deployment and MLOps concepts.
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Experience with cloud ML platforms such as Azure ML, Google Vertex AI or AWS SageMaker.
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Exposure to big data ecosystems and real-time data processing.
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Knowledge of regulatory and compliance considerations in financial services (preferably Insurance)