Senior Product Manager - Data Science

The Zebra•Austin, TX

18d•$120,000 - $140,000•Remote

About The Position

The Zebra revolutionizes how connected consumers research and shop for insurance. We intentionally strive to build diverse teams that feel inclusive for all. Our motto is "All Stripes Welcome," we put that into practice by valuing traditional and non-conventional backgrounds and perspectives. Our Zeebs are passionate about learning, growing, & working together to tackle exciting problems. Our Product Team is expanding, & we’re seeking a Sr Product Manager of Data Science to help support The Zebra’s growing needs. As our Sr Product Manager for Data Science, your mission is to transform cutting-edge models into seamless customer experiences. You will lead the charge on the full model lifecycle, from creative feature engineering to rigorous production monitoring and drift management. This isn’t a role for academic theory; it’s for a hands-on leader who thrives on the technical challenge of keeping AI performing at its peak in a live environment. Reporting directly to the VP of Product, you will work closely with the Chief AI Officer, the data science team and engineering to achieve company and AI Village goals. Location: Our employees can choose to work remotely in the U.S., join us in our Austin, Texas office, or opt for a hybrid setup. Whether you prefer the structure of an office, the convenience of remote work, or a mix of both, we support what works best for you! Please note - At this time we are not able to hire in California, Massachusetts, New Jersey and New York.

Requirements

3+ years experience in a consumer-facing product management or equivalent role
Experience working closely with engineers and designers in agile or scrum environments.
Strong analytical, problem-solving, and organizational skills.
Excellent written and verbal communication skills, including the ability to translate complex ideas for different audiences.
Customer-first mindset with a passion for creating intuitive, impactful products.
Proficiency in product tools like Jira, Confluence, Figma, and data tools like Looker, Amplitude, or similar.

Nice To Haves

Experience in insurance/insurtech is a plus; experience operating in a regulated industry (e.g., financial services, healthcare) is also strongly preferred.

Responsibilities

Define and own the product vision, strategy, and roadmap for data science and machine learning initiatives
Translate business problems into well-scoped data science use cases and product requirements
Partner closely with data scientists, data engineers, and ML engineers to deliver models and data products end to end
Prioritize features, experiments, and model improvements based on impact, feasibility, and risk
Lead discovery efforts to identify opportunities where data science can create meaningful business value
Ensure models are production-ready, monitored, and continuously improved post-launch
Collaborate with engineering and platform teams on data infrastructure, tooling, and scalability
Communicate clearly with stakeholders on trade-offs, timelines, model performance, and outcomes
Define success metrics and evaluate product performance using quantitative and qualitative insights
Ensure responsible use of data, including privacy, fairness, and ethical considerations

Benefits

Competitive Compensation & Stock Option Offering
Health, Dental, Vision & Disability Coverages
End early Fridays (wrap up work at 2pm CST on Fridays!)
Sabbatical program for tenured employees
HSA offering + employer contribution
Fertility support through Carrot
$100 monthly wellness perk
$300 pet reimbursement program
401k with match
Unlimited PTO (paid time off)+ flexibility to enjoy it
Paid Parental Leave Program
Learning & Development Opportunities
Join a team that truly lives their values (outside of the office. Cliche, we know… but we really mean it)
The base salary range for this position is $120,000 - $140,000 per year. The final salary amount will be determined based on a variety of factors, including experience, skills, internal equity, and insights gained throughout the interview process