Data Science Actuary

May 31

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Management & professional liability insurance for the 21st century workplace

insurance • management liability • technology • data • D&O

11 - 50

Description

• Build and maintain new efficient/expansive rating systems and related models in Excel and in Python • Build and maintain risk/simulation models to help analyze and steer the business • Research ways to leverage industry data to enhance current rating, new/current admitted filings, and provide guidance to underwriting • Develop complex analyses to our underwriting, operations, business development, product performance, and user experience. • Assist the data science, operations, and insurance teams with ad hoc analysis, data normalization, data cleansing and process improvement. • Support the integration and production of new data sources in a manner that minimizes development cycles while maximizing potential business applications. • Continuously challenge how we can improve our underwriting and operations. • Maintain a clean production environment such that the data models can be easily interpreted and built upon by other data science and engineering team members. • Present your work, findings, and opinions to both technical and non-technical stakeholders.

Requirements

• Minimum of 3 years of total work experience • Bachelor/Master in quantitative discipline (computer science, actuarial science, mathematics, statistics, economics, physics, engineering or related field). • Preferred but not required: ACAS, FCAS (this is a hybrid actuarial and analytics role) • Experience and interest with data scientist techniques (e.g. Machine Learning) • An entrepreneurial mindset, interested in building the future • Preferred: domain knowledge in management/professional liability • Ability to balance competing priorities and focus on key initiatives, by estimating timelines and keeping team/documentation updated with status of projects • Experience and excitement using modern cloud computing and cloud databases (i.e. AWS, Snowflake etc.) • A passion for solving challenging mathematical problems and an interest in exploring new machine learning tools and technologies. • 2+ years experience with Python. • Communications skills for translating technical or statistical analysis results into business recommendations. • Bias to practical action and creativity using data (we value past or present projects that support this).