Data Scientist with P&C Insurance Analytics experience

Develop analytical and predictive models using statistical and machine learning techniques.

Analyze large Property & Casualty insurance datasets to identify trends, patterns, and business insights.

Support benchmark development and insurance analytics initiatives.

Build and validate predictive models such as regression, decision trees, classification, and clustering models.

Work with structured and unstructured data from multiple sources.

Utilize Databricks and modern cloud-based analytics platforms for data processing and model development.

Perform exploratory data analysis (EDA) and communicate findings effectively.

Collaborate with business users, actuaries, data engineers, and analytics teams.

Present analytical findings and recommendations to technical and non-technical stakeholders.

Ensure data quality, model accuracy, and continuous model improvement.

We are looking for a Data Scientist (around 4-6 years) with a Property & Casualty insurance background who can build analytical and predictive models for insurance benchmarking. Experience with Databricks, AI/ML, and statistical modeling is important. Candidates with actuarial knowledge or exams are a strong advantage.

The ideal candidate will have hands-on experience applying statistical and machine learning techniques to solve business problems, along with exposure to modern data platforms such as Databricks.

This role requires someone who can analyse large insurance datasets, develop predictive models, generate actionable insights, and collaborate with business stakeholders to improve decision-making., Master's degree in Data Science, Data Analytics, Statistics, Computer Science, Mathematics, or a related quantitative field.

3-5 years of experience in Data Science or Advanced Analytics.

Strong experience within the Property & Casualty Insurance domain.

Experience developing predictive and statistical models.

Strong understanding of:

Regression Analysis

Decision Trees

Classification Models

Clustering Techniques

Neural Networks (preferred)

Experience with Python and SQL.

Hands-on experience with Databricks.

Knowledge of machine learning libraries such as Scikit-learn, XGBoost, TensorFlow, or PyTorch.

Strong analytical and problem-solving skills.

Excellent communication and presentation skills.

Preferred Qualifications

Actuarial exams or actuarial knowledge is highly preferred.

Experience supporting insurance benchmarking or pricing initiatives.

Exposure to Generative AI or AI-driven analytics.

Experience working with cloud platforms such as Azure or AWS.

Knowledge of commercial Property & Casualty insurance products.

Experience working with large-scale insurance datasets.

Nice to Have

Experience with Power BI or Tableau.

Knowledge of Spark and PySpark.

Experience building analytical dashboards.

Familiarity with MLOps concepts and model deployment.

Required Skills

Property & Casualty Insurance

Data Science

Machine Learning

Predictive Analytics

Statistical Modeling

Regression Analysis

Decision Trees

Neural Networks

Python

SQL

Databricks

Data Analytics

Preferred Skills

Actuarial Science, 3-5 years of Data Science experience.

Strong Property & Casualty insurance domain expertise.

Experience with AI, machine learning, and statistical modeling.

Comfortable working with modern analytics platforms such as Databricks.

Passionate about solving business problems through data-driven insights.

• 401(k)
• Dental insurance
• Health insurance
• Paid time off
• Vision insurance