Data Scientist II - Lifetime Value Analytics
CURRENT ROOT EMPLOYEES - Please apply using the career page in Workday. This career site is for external applicants only.
We believe that a disruptive insurance company must have a principled quantitative framework at its foundation. At Root, we are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.
A Data Scientist II at Root is responsible for the end-to-end development of statistical methods and algorithms. This includes taking high-level business challenges, translating them into a concrete, quantitative framework, and shepherding solutions from R&D into production. Data Scientists typically work on cross-functional teams, regularly engaging with the members of various departments including Product, Actuarial, Marketing, and Engineering.
The Lifetime Value (LTV) data science team is looking for a Data Scientist II to continuously improve the way we estimate customer LTV. Customer LTVs are at the heart of many decisions that we make across the company, from optimizing the onboarding flow to allocating marketing capital. Better estimates of LTV translate into better decision making.
Root is a “work where it works best” company. Meaning we will support you working in whatever location that works best for you across the US. We will continue to have our headquarters in Columbus and offices in other locations to give more flexibility and more choice about how we live and work.
Salary Range: $104,600 - $130,700
How You Will Make an Impact
Applying principled methods to forecast and validate customer lifetime value
Learning the required tools to get the job done, e.g., AWS, Git, etc.
Building data science pipelines to quickly iterate on research ideas and put them into production
Effectively communicating insights from complex analyses
Taking end-to-end ownership of problem domains and continuously improving upon quantitative solutions
What You Will Need to Succeed
Advanced degree in a quantitative discipline (PhD preferred) and/or 2+ years of applying advanced quantitative techniques to problems in industry
Strong demonstrable knowledge of topics such as statistical modeling, machine learning, and numerical optimization
Exceptional communicator and storyteller with strong data visualization skills
Strong programming skills with experience in SQL and Python
Demonstrated experience building, validating, and applying statistical machine learning methods to real world problems
Demonstrates ownership mentality, taking initiative to find, prioritize, and be accountable for the highest impact work
Ability to frame functional problem statements for the next 1-2 months, consistently making good decisions about the right path to follow in a well-defined problem space
Preferred but not required:
Experience using version control (Git) and cloud computing (AWS)
Basic understanding of survival / time series analysis
Insurance industry experience
Don’t meet every single requirement?
Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At Root, Inc., we are dedicated to building a diverse and inclusive workplace, so if you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway!
At Root, we judge people based on the merit of their work, not who they are. If you are passionate about what this role entails and solving real problems, we encourage you to apply. We want to learn about you and what you can add to our team.
Who we are
We’re harnessing the power of technology to revolutionize insurance. Using machine learning and mobile telematic platforms, we’ve built one of the most innovative FinTech companies in the world. And we’re just getting started.
What draws people to Root
Our success is in large part due to our unwavering standards in hiring. We recognize that our products are only as good as the people building and promoting them. We want individuals who find solutions by going through the cycle of ideation to implementation with curiosity, rigor, and an analytical lens. Ask anyone who works here and you’ll hear similar reasons for why they joined:
Autonomy—for assertive self-starters, the opportunities to contribute are limitless.
Impact—by challenging the way it’s always been done, we solve problems that have a big impact on our business.
Collaboration—we encourage rich discussion and civil debate at every turn.
People—we are inspired by the collection of crazy-smart people around us.