Data Science Actuary

DATA SCIENCE ACTUARY

Counterpart believes in small businesses and is dedicated to helping them do more with less risk. By pairing leading insurance experts with cutting-edge technology, Counterpart empowers small business owners to grow with confidence. Exceptional underwriters, trusted insurance brokers, and prominent insurance carriers come together on the Counterpart platform to support small businesses by providing AI-driven management and professional liability underwriting and claims services. That’s where you come in.

As a Data Science Actuary, you will balance being a prolific individual contributor to initiatives at the intersection of data science and traditional actuarial work. You will help bridge the gap between the Data & Insurance teams by leveraging actuarial techniques such as pricing and risk modeling as well as new techniques such as machine learning and programming. In addition, you will contribute to the culture, rituals, and processes that underpin a high functioning team and the organization more broadly. 

AS A DATA SCIENCE ACTUARY, YOU WILL: 

• 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. 

WE LOOK FOR TEAMMATES WHO HAVE: 

• 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 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). 

WHO YOU WILL WORK WITH: 

• Tanner Hackett, CEO & Founder: Having founded two other major startups, including Button and Lazada, Tanner now spends his time focused on mental health through his philanthropy,Openminded.org, in addition to reading, surfing, yoga, and enjoying the outdoors. 
• Tobias Schuler, Head of Data & Analytics Advisor: Tobias was previously the Head of Data and Analytics at Digital Partners, a Munich Re company. Tobias led a team that built out data integrations, business intelligence and advanced analytics across all insurtech partners spanning various P&C lines of businesses. Tobias is also a FCAS and has built systems to enable leading class insights for underwriting, actuarial, claims and finance experts while focusing on democratizing data. He enjoys traveling internationally and spending time with his 2 young daughters.
• Stanley Wang, Director, Pricing Analytics: Before joining Counterpart, Stanley worked as an actuary within the pricing solutions and methods team at USAA to combine actuarial pricing with data science models. Before that, he was a leading data scientist for Digital Partners, a Munich Re Company where he specialized in building data science models and insights for leading insurtech companies. He has had many other relevant roles such as capital modeling, risk management etc. He lives in New York with his growing family.
• Elizabeth Barsalou, Data Scientist: Before joining Counterpart, Elizabeth worked as a full stack data scientist in small business lending for Kabbage and BHG. She specialized in building data science models, infrastructure and strategies to extend credit to small businesses. She lives in San Francisco and spends her free time singing opera and playing with her dogs.
• Chris Shafer, Special Projects: Chris is a published scientific author, having studied the neural correlates of gratitude at the lauded Brain and Creativity Institute. He has since helped to launch numerous businesses in which he held a variety of critical roles from product management to business operations to strategic partnerships. Chris received his bachelor’s degree in Biological Sciences from the University of Southern California.

We are committed to being a welcoming and inclusive workplace for everyone, and we are intentional about making sure people feel respected, supported and connected at work—regardless of who you are or where you come from. We value and celebrate our differences and we believe being open about who we are allows us to do the best work of our lives. 

We are an Equal Opportunity Employer. We do not discriminate against qualified applicants or employees on the basis of race, color, religion, gender identity, sex, sexual preference, sexual identity, pregnancy, national origin, ancestry, citizenship, age, marital status, physical disability, mental disability, medical condition, military status, or any other characteristic protected by federal, state, or local law, rule, or regulation.