Data Scientist II, Actuarial Research
HQ - US - Columbus, United States
⚠️ We'll shut down after Aug 1st - try foo🦍 for all jobs in tech ⚠️
Full Time Mid-level / Intermediate USD 116K - 145K
Root Inc.
Root® does car insurance differently. We believe good drivers should pay less for auto insurance so we base rates primarily on how you drive. Get a free quote.CURRENT ROOT EMPLOYEES - Please apply using the career page in Workday. This career site is for external applicants only.
As part of Root's interview process, we kindly ask that all candidates be on camera for virtual interviews. This helps us create a more personal and engaging experience for both you and our interviewers. Being on camera is a standard requirement for our process and part of how we assess fit and communication style, so we do require it to move forward with any applicant's candidacy. If you have any concerns, feel free to let us know once you are contacted. We’re happy to talk it through.
The Opportunity
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 guiding 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 Actuarial Research team partners closely with Actuarial, Lifetime Value (LTV), and Forecasting to standardize key assumptions that inform Root’s profitability goals and to recommend business actions that support those targets. To advance this work, we are looking for a Data Scientist II to enhance how Root incorporates external factors—such as historical weather and economic data—as well as internal business decisions into profitability assessments. This role will also focus on developing conversion and retention models, enabling the team to anticipate customer response to proposed actions and ensure that recommended changes support both financial and customer outcomes.
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 to give more flexibility and more choice about how we live and work.
Salary Range: $116,664 - $145,830 (Bonus and LTI Eligible)
How You Will Make an Impact
Develop statistical and machine learning models to assess how pricing and business decisions influence policyholder conversion and retention.
Quantify the impact of external factors—such as economic conditions and weather events—on profitability, customer behavior, and market dynamics.
Integrate Root’s internal business actions (e.g., rating and underwriting changes) into profitability frameworks to enable more accurate performance projections.
Communicate insights from complex analyses in clear, actionable terms to cross-functional stakeholders, ensuring alignment and effective decision-making.
Take ownership of problem domains and continuously refine quantitative solutions to ensure they are robust, scalable, and impactful over time.
What You Will Need to Succeed
Advanced degree in a quantitative discipline (Master’s or PhD preferred) and 2+ years of experience applying advanced quantitative techniques, ideally in the insurance industry
Strong programming skills with experience in SQL and Python
Demonstrates ownership mentality, taking initiative to find, prioritize, and be accountable for the highest impact work
Strong communication and data storytelling skills, with the ability to visualize insights and clearly explain complex technical concepts to both technical and non-technical stakeholders
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)
Familiarity with elasticity models and related econometric techniques
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!
Join usAt 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 areWe’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 RootOur 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.
Tags: AWS Engineering FinTech Git Machine Learning ML models PhD Python R R&D Research SQL Statistics
Perks/benefits: Career development Team events
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