Responsibilities :

• Actuarial Science Tools & Techniques: Proficient in frequency and severity modeling, survival analysis, and risk assessment methodologies used in general insurance and financial services.
• Programming & Data Manipulation: Skilled in Python (Pandas, NumPy), R (actuarial science packages), SQL, and Excel for data manipulation, cleaning, and ETL processes.
• Statistical & Machine Learning Modeling: Experienced with machine learning libraries like Scikit-Learn, TensorFlow, and actuarial libraries such as lifelines (for survival analysis) and actuarial-specific packages in R.
• Data Science Libraries: Advanced knowledge of Python libraries like Pandas, Seaborn, and Matplotlib for data wrangling and visualization; capable of building complex data science models.
• Insurance Pricing & Risk Models: In-depth understanding of general insurance pricing, stochastic modeling, and advanced actuarial techniques for accurate risk assessment and pricing.
• Data Visualization: Proficient in creating data-driven visualizations using tools such as Power BI, Tableau, and Seaborn, transforming technical findings into meaningful insights for non-technical audiences.