Manager-Data Scientist-Insurance Domain

• 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 modelling 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.