Data Scientist with P C Insurance Analytics experience
Role overview
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.
Responsibilities
- check_circle Develop analytical and predictive models using statistical and machine learning techniques.
- check_circle Analyze large Property & Casualty insurance datasets to identify trends, patterns, and business insights.
- check_circle Support benchmark development and insurance analytics initiatives.
- check_circle Build and validate predictive models such as regression, decision trees, classification, and clustering models.
- check_circle Work with structured and unstructured data from multiple sources.
- check_circle Utilize Databricks and modern cloud-based analytics platforms for data processing and model development.
- check_circle Perform exploratory data analysis (EDA) and communicate findings effectively.
- check_circle Collaborate with business users, actuaries, data engineers, and analytics teams.
- check_circle Present analytical findings and recommendations to technical and non-technical stakeholders.
- check_circle Ensure data quality, model accuracy, and continuous model improvement.
Basic qualifications
- 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.
Benefits
- check_circle 401(k)
- check_circle Dental insurance
- check_circle Health insurance
- check_circle Paid time off
- check_circle Vision insurance
Tags & Focus Areas
About Bourntec Solutions
We are seeking a Lead Machine Learning Engineer to spearhead the design, development, and deployment of AI/ML-driven solutions that enhance automation, insights, and decision-making across insurance operations. This role combines hands-on machine learning expertise, leadership responsibilities, and exposure to cutting-edge GenAI and LLM-based frameworks. The successful candidate will lead a small, high-performing team to research, prototype, and operationalize AI capabilities for real-world...
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