Senior Data Scientist

PhD or MS degree in Computer Science, Applied Mathematics, Statistics, Engineering, or similar quantitative disciplines.
Mastery in developing and maintaining end-to-end data and modeling pipelines using AWS Glue, Airflow, and Sagemaker.
Strong programming skills in Python and fluency in data manipulation (SQL, pandas, pyspark) and Machine Learning (Scikit-learn, statsmodel, XGBoost and etc) tools.
Experience working with AWS (or similar) tools/services like Athena, S3, EC2, Redshift, etc.
5+ years of experience in statistical data analysis, feature engineering, predictive modeling, and data visualization
3+ years of P&C (Property & Casualty) Industry experience
Excellent written and verbal communication skills.
Ability to think critically and creatively in a dynamic environment, while picking up new tools and domain knowledge along the way.
A positive attitude and a growth mindset.

Develop, calibrate, validate, and deploy for production cyber risk models for the (re)insurance and financial services markets.
Develop and implement methodologies to quantify the financial impact of cyber risk on single entities as well as large insured portfolios.
Develop and implement tools to effectively visualize the potential impact of cyber events.
Explore different data sources to come up with features and assumptions to enhance our set of probabilistic risk models.
Integrating/automating modeling processes in our production pipeline to feed our platform.
Define and implement a globally consistent best practice process for data validation, feature selection, and modeling of catastrophe exposures.
Champion best practices to design and extend a rapid and flexible modeling framework.
Communicate results to internal and external stakeholders.
Engage and collaborate directly with clients on a deep technical level.
Collaborate with other leaders across the Analytics organization, including Product Management, Engineering, and Client Engagement.