Posted:6 days ago|
Platform:
Work from Office
Full Time
Model Development & Analytics
Build predictive and machine learning models for risk assessment, cross-sell/upsell, customer retention, and claims forecasting.
Design and implement fraud detection algorithms and anomaly detection frameworks.
Support pricing and reserving teams with statistical models and simulations.
Data Management & Exploration
Clean, process, and analyse structured/unstructured insurance data (policy, claims, customer, telematics, digital).
Perform exploratory data analysis (EDA) to identify patterns, trends, and actionable insights.
Business Problem Solving
Partner with Underwriting, Claims, and Distribution teams to translate business challenges into analytical problems.
Provide insights on loss ratios, claim frequency/severity, and customer behaviour.
Reporting & Visualization
Create dashboards and visualizations (Power BI/Tableau) for key business metrics (loss ratios, premium trends, fraud triggers, claim settlement times).
Present findings and recommendations to stakeholders in a clear, actionable manner.
Strong knowledge of statistical modeling, machine learning, and predictive analytics.
Proficiency in Python / R / SAS for data analysis and modeling.
Hands-on experience with SQL and large datasets.
Experience with data visualization tools (Power BI, Tableau, Qlik).
Good understanding of general insurance concepts (pricing, underwriting, claims, fraud, loss ratio).
Strong communication skills to explain technical results to business users.
Bachelors/Masters in Statistics, Mathematics, Data Science, Actuarial Science, or related field.
Exposure to NLP, computer vision, or telematics data in insurance.
Knowledge of cloud platforms (AWS, Azure, GCP) for model deployment.
Improvement in loss ratio and fraud detection rate.
Accuracy and adoption of predictive models by business teams.
Value generated through cross-sell/upsell models.
Timely and effective delivery of analytical insights.
SBI General Insurance
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