Manager – Responsibilities:
- Data Analytics and Insights:
- Conduct analytics to identify patterns and generate actionable insights to support strategic decisions.
- Process Unstructured data to drive actionable insights.
- Translate quantitative analyses into comprehensive visuals and reports for non-technical audiences.
- Model Development and Validation:
- Build, validate, measure, and retrain machine learning models, including supervised and unsupervised algorithms.
- Apply expertise in Natural Language Processing (NLP) and Generative AI to solve complex business challenges.
- Deployment and Collaboration:
- Collaborate with AI Engineers to deploy machine learning models and set up inference processes.
- Ensure models are scalable, maintainable, and aligned with organizational goals.
- What Will you focus on :
- Risk Assessment and Pricing: Developing predictive models to evaluate risks and set accurate premiums. By analyzing historical data, they identify patterns that inform underwriting decisions.
- Fraud Detection: Implementing machine learning algorithms to detect fraudulent activities by identifying anomalies in claims data. This proactive approach helps in minimizing losses due to fraud.
- Customer Segmentation and Personalization: Analyzing customer data to segment the market and tailor insurance products to specific groups, enhancing customer satisfaction and retention.
- Claims Management Optimization: Utilizing data analytics to streamline the claims process, ensuring timely and accurate settlements. This includes predicting claim volumes and identifying potential bottlenecks.
- Marketing Strategy Enhancement: Assessing the effectiveness of marketing campaigns and identifying opportunities for customer acquisition and retention
Requirements:
- Experience: 6-7 years of experience in Insurance analytics or a related domain
- Education: bachelors degree in Engineering, Statistics, Mathematics, Computer Science, or a related quantitative field.
- Proficiency in programming languages and data analysis tools such as Python, R, PySpark, and SQL.
- Solid experience in developing predictive modeling techniques (look-a-like models, time series forecasting, regression, clustering)
- Ability to design, implement, and refine business rules for optimizing the Claims and Underwriting value chains is a good to have.
- Familiarity with working in cloud environment (AWS/ AZURE), using distributed compute for large datasets, and version control tools (eg Git)
- Data Proficiency: Expertise in handling large-scale Insurance datasets and applying statistical and machine learning methods to drive actionable insights.
- Data Storytelling & Communication: Demonstrated ability to translate complex data insights into clear, compelling narratives and presentations. Adept at communicating technical findings in a relatable manner to non-technical stakeholders.
- Autonomy & Prioritization: Proven ability to work independently, manage multiple projects/workstreams, and prioritize effectively in a fast-paced, data-driven environment.
- Problem-Solving & Collaboration: Demonstrated ability to troubleshoot complex data issues, optimize system performance, and work effectively within a team environment.