- PhD or MS degree in Applied Mathematics, Statistics, Engineering, or similar quantitative disciplines.
- Catastrophe Risk Model experience
- Proficiency in Python or R, and SQL.
- Experience working with tools such as AWS Athena, Databricks, Apache Airflow, etc.
- Mastery of deep learning non-parametric models, clustering, anomaly detection, and interpretability.
- 5+ years of experience in statistical data analysis, feature engineering, data visualization, and hypotheses testing.
- True passion for leading, inspiring, mentoring, and attracting world-class colleagues.
- Experience working with different types of datasets (e.g., unstructured, semi-structured, with missing information).
- 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 stress test probabilistic 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, with a specific focus on digital/physical supply chain-related events.
- 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.
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Lead Data Scientist, Cyber Catastrophe Risk Model - San Mateo, United States - Guidewire Software
Description
Guidewire Software's (NYSE: GWRE) Cyence team is searching for a Lead Data Scientist with Catastrophe Risk Model experience to join us on our mission to transform cyber-insurance with the industry's leading cyber risk platform. Cyber is the #1 threat to US national security, above nuclear weapons, and our work here makes a meaningful impact in this space You will report to the Director of Risk Modeling.
Who We Are, What We Believe, & What We Build
Guidewire is the AWS of insurance. As the market leader, 540 insurance companies run on our mission-critical platform. Every second, we support underwriters crafting policies and agents settling claims. We believe that making a great decision should not require 100 in-house data scientists. Our products range from cyber risk quantification to potent ML sandboxes. We are a post-IPO company with the vision to redefine insurance.
Who You Are
You are a passionate, detail-oriented, and creative problem solver with a strong background in Data Science and Engineering, excited by the prospect of pursuing hard problems and exploring the unknown.
You enjoy applying advanced quantitative methods such as statistical modeling, machine learning algorithms, numerical simulations, and optimization techniques to analyze complex datasets and build predictive models. Experience in CAT modeling is a plus
What we looking for:
The Role:
