Lead Applied Scientist, Marketing

MUST HAVE:

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

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

YOUR ROLE

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.

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