Lead Data Scientist

Role Description

Own the full data science engine for a priority vertical, from business problem to deployed model to live ROAS performance, driving measurable revenue and media efficiency. This is a hands-on, in-the-weeds role: you are heavily immersed in the data and the modeling, framing the business problem directly with stakeholders, building and validating the model, handing the ML-engineering last mile to your ML engineering partner, and staying engaged through deployment, monitoring, and performance analysis.

You will start focusing on Insurance and Advertiser Quality, with scope that broadens over time. Your primary metric is ROAS.

Qualifications

• Proven experience in digital marketing, performance marketing, or the leadgen industry
• Building adtech algorithms and supporting user acquisition or paid media modeling (highly desired)
• Strong modeling fundamentals: the ability to build effective models that drive business impact
• Multi-year, hands-on experience building and deploying ML solutions in the AWS cloud
• Hands-on experience across core technique areas: multi-armed bandit / reinforcement learning, recommendation and ranking systems (content-based, collaborative filtering, hybrid), funnel and monetization optimization, LTV modeling
• Expert Python and SQL

Requirements

• 5+ years in a hands-on, in-the-weeds applied data science role delivering measurable business impact.

Outcomes

• Own the Insurance vertical's primary modeling work end-to-end with measurable ROAS impact
• Deliver buying models that maintain positive ROAS and quality
• Drive lead quality improvements across our portfolio of brands: Messaging, Funnels, Content/Listicles, and more resulting in measurable impact to revenue growth
• Establish trusted, direct partnership with vertical business stakeholders
• Produce trusted output: validated, documented, low correction burden
• Identify and leverage net-new modeling opportunities the business has not flagged

Competencies

• Business-first framing: Starts with the problem and the metric, not the model.
• Full-stack ownership: Stays engaged from problem definition through deployed performance
• Proactive communication: Closes loops without being chased
• Collaborative: Leans on ML engineering for the last mile rather than working solo
• Coachable: Seeks feedback and turns it into visible behavior change
• Curiosity paired with delivery discipline

Nice to Haves

• Sophisticated ML at companies where paid digital media is core to the business model
• Creative embeddings work: incorporating embeddings of creatives, videos, headlines, and search into paid media models
• Insurance domain experience
• Creating state-of-the-art Ad Ranking algorithms
• Modeling against ad-platform data points (Google, Meta, native)
• LLMs / deep learning applied to personalization or content
• Familiarity with Looker