The position.
We believe a large part of building an effective insurance company can be solved with a principled quantitative framework. We are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.
The Lifetime Value (LTV) team creates models that predict conversion, retention, and loss cost. The team also builds and maintains tools that bring these models together to predict the lifetime value (and other related key metrics) of Root customers. We are looking for a Data Analyst to work closely with our Data Scientists to increase the maturity of our model development lifecycle through iterative improvements to both pre-and post-deployment analytics. You'll help to validate and improve our data sources while assisting our data scientists in developing deeper insights and understandings across various analytical projects, identifying trends and impacts across our data. You'll also be a part of building critical LTV KPIs, helping a wide range of stakeholders stay up-to-date on what's happening.
The ideal candidate will have deep analytical and business acumen, strong programming skills, and high quantitative aptitude.
What you’ll be doing.
- Generate and interpret descriptive statistics to make business recommendations.
- Validate and improve core LTV data strategies.
- Standardize and automate the analytics pipeline for use across the LTV organization.
- Analyze large datasets to derive key insights into the lifetime value of our Root customers.
- Develop and maintain the key documents and dashboards on core LTV models.
- Establish standard reporting to share with key stakeholders.
- Monitor a suite of dashboards and metrics through manual and automated means, diving deeper and/or notifying stakeholders as makes sense if results do not look as expected.
What we’re looking for.
- 2+ years of experience in data analytics in a business or academic environment.
- Strong programming skills, Python preferred.
- Advanced skills in SQL with the ability to write complex and optimized queries.
- Intermediate to advanced skills in Tableau or a similar dashboarding tool.
- Experience with predictive modeling and model diagnostics.
- Knowledge of basic statistical techniques.
- Analytical mindset, with strong problem solving and critical thinking skills.
- Exceptional communicator and storyteller.