EXPERIENCE: 10–18 years (minimum of 5 years in insurance domain)

WORK MODE: Remote Role Overview

We are seeking a Senior Data Scientist – Insurance domain to drive advanced analytics initiatives across the client's insurance portfolios. This is a hands-on IC role requiring deep insurance domain expertise, robust technical skills in machine learning, and the ability to design, deploy, and monitor predictive models that deliver measurable business value. Key Responsibilities

Insurance Analytics: Build and deploy ML models for motor, health, family, and other insurance lines, focusing on risk scoring, claims prediction, fraud detection, and customer segmentation.

Marketing Analytics: Develop predictive models for customer acquisition, retention, and cross-sell/upsell strategies; optimize loyalty and campaign ROI.

Model Development & Deployment:

Design end-to-end pipelines in Python (scikit-learn, TensorFlow, PyTorch).

Implement MLOps frameworks for deployment, monitoring, and retraining.

Ensure model explainability (SHAP, LIME) for regulatory and business transparency.

Risk & Compliance: Integrate models with insurance workflows ensuring compliance with PCI DSS, AML/KYC, and scheme rules.

Data Engineering: Work with structured/unstructured data, build scalable pipelines, and ensure data quality across multiple sources.

Monitoring & Governance: Establish dashboards for model drift, performance monitoring, and bias detection; ensure continuous improvement.

Stakeholder Engagement: Collaborate with actuarial, underwriting, claims, marketing, and IT teams to translate business needs into analytics solutions.





Leadership: Mentor junior data scientists, drive agile delivery, and present insights to senior management (HLIs – High-Level Insights).

Qualifications & Skills

Experience: 10–18 years in data science/analytics, with at least 5+ years in insurance domain (motor, health, family, risk analytics).

Technical Skills: Python, SQL, ML frameworks (TensorFlow, PyTorch, scikit-learn), MLOps tools (MLflow, Kubeflow, Airflow), API integration.

Insurance Knowledge: Deep understanding of underwriting, claims, fraud, and customer lifecycle in insurance.

Analytics Tools: Power BI, Tableau, SAS for reporting and visualization.

Soft Skills: Agile mindset, dynamic communicator, ability to influence stakeholders and drive measurable business outcomes.

Education: Master’s/PhD in Data Science, Statistics, Computer Science, or related field. Insurance certifications (e.g., actuarial, risk management) preferred.

ML Models – Clustering/segmentation, Decision Tree, Logistic Regression, Linear regression, ARIMA time series forecasting , Anamoly detection models, Random forest, XGB models, survival models etc, A/B testing.

What Success Looks

Like

Predictive models deployed across motor and health insurance portfolios with measurable impact on loss ratios, claims efficiency, and fraud detection.

Marketing analytics driving higher customer acquisition, retention, and campaign ROI.

Robust monitoring pipelines ensuring model reliability, compliance, and explainability.

Clear communication of HLIs (High-Level Insights) to executives, enabling data- driven decision-making

📌 Senior Data Scientist (India)
🏢 Hyper Lychee Labs
📍 India