Lead Data Scientist, Cyber Catastrophe Risk Model

3 weeks ago


San Mateo, United States Guidewire Software Full time

Guidewire-Cyence is searching for a **Lead Data Scientist** 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.

**Responsibilities**:

- ** 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.

**Qualifications and Requirements**:

- PhD or MS degree in Applied Mathematics, Statistics, Engineering, or similar quantitative disciplines.
- Mastery of deep learning non-parametric models, clustering, anomaly detection, and interpretability.
- 10+ 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).
- Proficiency in Python or R, and SQL.
- Experience working with tools such as AWS Athena, Databricks, Apache Airflow, etc.
- 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.

**About Guidewire**

Guidewire is the platform P&C insurers trust to engage, innovate, and grow efficiently. We combine digital, core, analytics, and AI to deliver our platform as a cloud service. More than 540+ insurers in 40 countries, from new ventures to the largest and most complex in the world, run on Guidewire.

Guidewire Software, Inc. is proud to be an equal opportunity and affirmative action employer. We are committed to an inclusive workplace, and believe that a diversity of perspectives, abilities, and cultures is a key to our success. Qualified applicants will receive consideration without regard to race, color, ancestry, religion, sex, national origin, citizenship, marital status, age, sexual orientation, gender identity, gender expression, veteran status, or disability. All offers are contingent upon passing a criminal history and other background checks where it's applicable to the position.



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