Data Science Product Manager, Yield Optimization - Remote

Overview

Trellis is a profitable, fast-growing Series A startup backed by General Catalyst, QED, NYCA, and Amex Ventures. We’re reimagining how people shop for home and auto insurance, using smart technology to make something historically painful feel refreshingly easy.

Every day, we help people save time, money, and unnecessary headaches. Behind the scenes, our fully remote team moves fast, owns their craft, and builds products that genuinely make life better.

Led by a three-time fintech founder with a proven track record of going big, we’re building something special, and we’re just getting started.

If you’re hungry to make an impact and love solving complex problems with a scrappy crew, you’ll love it here.

Your Mission

We are seeking a Data Science Product Manager, Yield Optimization to lead the development, implementation, and ongoing improvement of predictive models that are central to our marketplace and revenue generation by ensuring that we show the optimal offers to our users. This critical role is focused on continuously improving decision-making for every individual user—from the moment they arrive to their final placement with an insurance provider—directly impacting funnel optimization and our revenue engine.

This is a highly cross-functional role where you will collaborate closely with internal Engineering and business stakeholders to turn messy, real-world data into clear insights and working models, while also interfacing externally with our ad and call buyers. Success requires a blend of product ownership, business acumen, and full-stack data science proficiency to define problems, prioritize opportunities, and ensure the models you own drive maximum business impact.

This is a fully remote position, based in the US or Canada, and will report directly into Keith Schreer, VP of Data. 

What You'll Do:

1. Lead Product & Planning: Partner with Engineering and Business teams to seamlessly integrate models into our recommendation engine.
2. Own Predictive Model Roadmap: Drive the full lifecycle of our yield optimization models, from ideation and development to production monitoring and iteration.
3. Drive Revenue: Continuously evaluate and improve model accuracy and interpretability to directly impact key business KPIs like revenue and conversion rates.
4. Cultivate a Thriving Marketplace: Use sophisticated models to intelligently match users to the best offers, driving value for our internal agency and external buyers.
5. Drive External Partnership Success: Interface directly with external partners, ensuring mutual success and aligning model performance with partner goals.

What You'll Need to Succeed:

• 7+ years of experience in Data Science roles implementing predictive models.
• Proven Full-Stack Data Proficiency: end-to-end ownership of predictive models used in production.
• Experience with recommendation systems, ad tech, or yield optimization models.
• Expert proficiency in SQL and the ability to independently source, wrangle, and understand data across the full stack.
• Business & Product Acumen: Strong ability to translate complex business problems (like optimizing revenue) into clear analytical questions and communicate insights/tradeoffs to non-technical stakeholders and external partners.
• High Autonomy & Execution: Demonstrated capacity to operate with high autonomy, follow through on unowned problems, and thrive in a fast-paced, scrappy startup environment.
• Communication: Clear, concise, and professional written and verbal communication for both internal alignment and external partner management.
• Preferred: Experience with ML platforms (e.g., DataRobot) or a background in Ad Tech/Insurtech is a plus.