USE YOUR DATA MODELING EXPERIENCE TO HELP DRIVE INFORMED BUSINESS DECISIONS
Why you should join our Modeling team:
As a Principal Data Scientist on the Modeling team, you will work under the Risk function alongside talented team members in pricing, risk analytics, and decision engines, while closely collaborating with the Cyber Research, Insurance and Tech Product, Claims, and Underwriting teams. The Modeling team at At-Bay has defined our aggregation view, developed an aggregation risk model, and supported reinsurance and finance in driving profitable growth. We are seeking a key team member to unlock new possibilities in attritional modeling, enhancing our segmentation and underwriting strategy.
In this exciting role, you will be the first to apply data scientist knowledge and skill set to pioneer new methodologies in the attritional side of the risk domain, playing a crucial part in shaping At-Bay’s risk model. You’ll join a growing team of data scientists and modelers of diverse backgrounds and report to our Director in Risk Modeling (Yoshi Yamamoto). You’ll be surrounded by a team that loves what they do, leverages technology to improve efficiency & minimize duplicative work, and recognizes the enormous responsibility that they have – to support key business decisions with data backed insights and a deep understanding of insurance risk. To learn more about the hiring manager, click here to review some white papers.
Role overview:
Your work will directly contribute to At-Bay’s risk assessment framework that helps with data-driven decisions involving millions of dollars of exposure. This is a multidisciplinary role that includes developing deep knowledge of the intersection between cyber security and insurance, business acumen, research, and analytical skills. This is also a hands-on technical role not only for leading and managing projects but also conducting data analysis/modeling end to end.At-Bay is in a unique position owning cyber security data and cyber insurance data including claims.
You will be responsible for developing an end to end data analytics platform. With the platform, you will lead the data analytics/research project on all the data available connecting to cyber security and cyber insurance, and provide the insight from data to cross-functional stakeholders including but not limited to Risk, Insurance & Tech Product, Underwriting, and Claims teams.
How you’ll make an impact:
By 3 months…
- You’ll understand At-Bay’s internal data structure/platform and develop key relationships across the business
- You’ll identify areas of improvement in current data structure/platform and internal/external data sources
- You’ll design the data platform and process for rapid prototyping predictive model connecting internal and external data to cyber incidents
By 6 months...
- You’ll have developed data platform for rapid prototyping
- You’ll have applied Gen AI and extracted useful information from existing data sources
- You’ll be sharing data and insights with our risk analytics and pricing team to inform our segmentation and underwriting strategy.
- You’ll have initiated new data project, performed data analysis, and provided insight to Risk team as well as Cyber research, Security, and Data Science teams
What you’ve accomplished already:
- You’ve led data science projects from end to end, collaborating and communicating cross functionally to ensure success
- You have experience designing and developing data pipeline to support various data analytics
- You have experience applying Gen AI to extract insight from unstructured data and data science techniques as follows; Clustering, Anomaly detection, Time series analysis, supervised/unsupervised Machine learning models
- You’ve achieved Bachelor’s or Master’s degree in computer science, data science, or other related field
- You’ve achieved proficiency with Excel and SQL, and have experience with Python and/or R
Location: This is a hybrid position based in SF Bay AreaOur estimated base pay range for this role is $175,000-$200,000 per year. Base salary is determined by a variety of factors including but not limited to market data, location, internal equitability, domain knowledge, experiences and skills. In general, if the position sparks your interest we encourage you to apply - our team prioritizes talent.
