Develop Lead Scoring algorithms that predict the LTV of qualified insurance leads, and pass it back to our ad partners to optimize bidding strategies, as well as to our internal Sales team to prioritize outreach.
Develop models to predict churn and claims losses across our insurance products. Work closely with the Underwriting team to ensure these models are integrated into business processes.
Assist in the development of a dynamic pricing model to help RV marketplace hosts adjust prices seamlessly in response to seasonal and vehicle-based factors, as well as surges in demand.
Develop models to predict what ancillary products should be recommended for upsell to a given Outdoorsy / Roamly customer.
Use advanced statistical methods to conduct audience clustering and segmentation on both RV Rental and Insurance customers, and evangelize these findings across stakeholders in Marketing and Product.
Collaborate closely with Data Analysts and Engineers to ensure that data pipelines and models are accurate and optimized.

Requirements

Industry Expert: 3+ years data science experience, with a proven track record of standing up Machine Learning algorithms within consumer-facing products (Travel, marketplace, or insurance experience preferred)
Business-Focused: Proven ability to understand business imperatives and trade-offs (Bookings vs. Revenue, Premium vs. Risk) and tailor outputs to align with company goals.
Collaborative Team Player: You work effectively across departments, e.g. Product, Engineering, and Finance, and understand that great products come from diverse perspectives.
Velocity-Focused: You thrive in fast-paced environments where priorities shift quickly, and you're energized by solving real customer problems.
Technical Proficiency: Advanced expertise in SQL, Python/R, and relevant machine learning libraries. Demonstrated experience with statistical methods such as regression, hypothesis testing, clustering, classification, tree-based methods, neural networks, and anomaly detection. Familiarity with ELT tools such as FiveTran and DBT, and ability to collaborate with data counterparts on data repositories and models. Expertise with business intelligence tools such as Tableau (preferred).
Academic Background: Degree in a quantitative field like data science, mathematics, economics, or computer science, or equivalent practical experience (Masters preferred)

Benefits

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Applicant Tracking System Keywords

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Hard Skills & Tools

Machine LearningLead Scoring algorithmsChurn predictionClaims loss predictionDynamic pricing modelAudience clusteringSegmentationStatistical methodsSQLPython

Soft Skills

Business-focusedCollaborativeTeam playerVelocity-focused