As a Data Scientist with 4 to 6 years of experience, you will be working at an exciting InsurTech startup focused on building an AI-native stack for the insurance sector. Your primary role will involve working on problems related to predicting customer drop-off (churn), risk scoring, and customer behavior analysis in insurance and financial services. **Roles and Responsibilities:**
Build models to predict customer churn, risk, and payment behavior
Conduct customer behavior analysis including cohorts, retention, and payment patterns
Generate meaningful features from raw data such as transactions and customer activity
Evaluate models for practical business use and collaborate with product/business teams for implementation
Monitor model performance and iterate based on real-world feedback
**Required skills:**
36 years of experience in data science and applied machine learning
Strong knowledge of supervised learning and feature engineering
Proficiency in working with messy real-world data, especially time series or transactional data
Understanding of model evaluation beyond accuracy and consideration of practical trade-offs
Ability to approach problems from a business perspective, focusing on real-world applications
Experience in BFSI or insurance domains is advantageous but not mandatory
**Nice to have:**
Previous experience with risk models, churn prediction, or customer analytics
Familiarity with concepts like cohort analysis, class imbalance, and model monitoring
Comfortable working with SQL and Python libraries such as pandas and scikit-learn
Join this innovative startup and contribute to the development of cutting-edge AI solutions for the insurance industry. As a Data Scientist with 4 to 6 years of experience, you will be working at an exciting InsurTech startup focused on building an AI-native stack for the insurance sector. Your primary role will involve working on problems related to predicting customer drop-off (churn), risk scoring, and customer behavior analysis in insurance and financial services. **Roles and Responsibilities:**
Build models to predict customer churn, risk, and payment behavior
Conduct customer behavior analysis including cohorts, retention, and payment patterns
Generate meaningful features from raw data such as transactions and customer activity
Evaluate models for practical business use and collaborate with product/business teams for implementation
Monitor model performance and iterate based on real-world feedback
**Required skills:**
36 years of experience in data science and applied machine learning
Strong knowledge of supervised learning and feature engineering
Proficiency in working with messy real-world data, especially time series or transactional data
Understanding of model evaluation beyond accuracy and consideration of practical trade-offs
Ability to approach problems from a business perspective, focusing on real-world applications
Experience in BFSI or insurance domains is advantageous but not mandatory
**Nice to have:**
Previous experience with risk models, churn prediction, or customer analytics
Familiarity with concepts like cohort analysis, class imbalance, and model monitoring
Comfortable working with SQL and Python libraries such as pandas and scikit-learn
Join this innovative startup and contribute to the development of cutting-edge AI solutions for the insurance industry.
