Senior Data Analyst

Role Overview
We are seeking highly skilled Analytics & Data Science professionals (5–7 years of experience) with strong domain expertise in Insurance/Financial Services. The role focuses on developing data-driven solutions across Fraud Detection, Risk Prediction, and Underwriting Analytics.

Key Responsibilities
Develop and deploy machine learning models for fraud detection, underwriting risk, and customer risk profiling.
Build scalable data pipelines and feature engineering frameworks using Python and SQL.
Design and implement end-to-end ML workflows including model training, validation, and deployment.
Work with business stakeholders to translate business problems into analytical solutions.
Perform exploratory data analysis (EDA), model monitoring, and performance tuning.
Create impactful dashboards and reports using Power BI/Qlik Sense for decision-making.
Collaborate with data engineering and IT teams for production-grade model deployment.
Required

Skills & Qualifications
5–7 years of experience in Analytics & Data Science roles.
Domain experience in Insurance or Financial Services (Fraud, Risk, Underwriting preferred).
Strong programming skills in Python and SQL.
Hands-on experience with machine learning techniques and model lifecycle management.

Experience with visualization tools such as Power BI/Qlik Sense.
Understanding of ML deployment and MLOps concepts.
Good to Have

Experience with cloud ML platforms such as Azure ML, Google Vertex AI or AWS Sage Maker.
Exposure to big data ecosystems and real-time data processing.
Knowledge of regulatory and compliance considerations in financial services (preferably Insurance)
Key Competencies
Strong analytical and problem-solving skills.
Ability to work in a fast-paced, business-driven environment.
Excellent communication and stakeholder management skills.