Cyber Risk Data Science Intern

Guidewire • San Mateo, CA

19h • $52,000 - $78,000 • Onsite

About The Position

As a Cyber Risk Data Science Intern, you will work at the intersection of cybersecurity, financial risk modeling, and machine learning. You will join a high-caliber multidisciplinary team of Data Scientists, ML Engineers, and Data Engineers, contributing to the next generation of probabilistic risk models that help the insurance industry quantify the financial impact of cyber-attacks. This is a high-growth learning opportunity where you will be directly mentored by one of our Principal Modelers, providing you with deep technical guidance and industry insights as you transition from academia to production-level data science. Job Description Location: Guidewire HQ, San Mateo, CA Duration: 6 months (June - November) Want to join Guidewire Basecamp? A place for you to belong and thrive. Guidewire Basecamp is our graduate and intern program. Designed through research from our own former interns and graduate level employees, Guidewire Basecamp is the program that all our interns and graduates become a part of when they walk through our doors. We give them the tools to leap further and find their own way, so they can confidently Navigate What's Next in their career. GWBC = Guidewire Basecamp

Requirements

• Education: Degree Preferred - Currently pursuing a Master’s or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Statistics, or a related quantitative field.
• Programming Proficiency: Strong skills in Python and fluency in data manipulation tools (SQL, pandas, or PySpark).
• Machine Learning: Academic or project-based experience with ML libraries such as Scikit-learn, XGBoost, or Statsmodels.
• Cloud Awareness: Familiarity with AWS environments (S3, EC2, Athena) and an interest in learning MLOps tools like Airflow or SageMaker.
• Analytical Rigor: Ability to think critically about data quality and apply creative problem-solving skills to complex datasets and extreme disaster scenarios.
• Communication: Strong verbal and written skills to share findings with a technical team.
• Growth Mindset: A positive attitude and a desire to leverage AI and data-driven insights to solve real-world financial risks.
• Demonstrated ability to embrace AI and apply it to your current role as well as data-driven insights to drive innovation, productivity, and continuous improvement.

Nice To Haves

• Experience with probabilistic modeling or catastrophe modeling.
• Prior internship experience in a data-centric role.
• Familiarity with cybersecurity concepts or the Property & Casualty (P&C) insurance industry.

Responsibilities

• Model Development & Validation: Assist in calibrating and testing cyber risk models used in the (re)insurance and financial services markets.
• Impact Quantification: Help implement methodologies to simulate the financial impact of cyber events on single entities and large-scale portfolios.
• Feature Engineering: Explore diverse, "noisy" datasets (unstructured and semi-structured) to discover new features that enhance our probabilistic models.
• Data Visualization: Develop and refine tools to effectively communicate the potential cascading impacts of cyber catastrophes.
• Production Integration: Support the team in automating modeling processes within our production pipeline to feed the Guidewire platform.
• Cross-Functional Collaboration: Engage with ML and Data Engineers to ensure models are scalable, efficient, and well-integrated.

Benefits

• All full-time positions or part-time roles working 30 hours or more a week at Guidewire are eligible for benefits that support their health and well-being including health, dental, and vision insurance, paid time off, and a company sponsored retirement plan.
• In addition, some roles may be eligible for the annual company bonus plan, commissions, and/or long term incentive awards which are contingent on a variety of factors including, but not limited to, company and employee performance.

What This Job Offers

• Job Type: Full-time
• Career Level: Intern