Responsibilities
- 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.
- Frame the business problem directly with stakeholders, build and validate the model, hand the ML-engineering last mile to your ML engineering partner, and stay engaged through deployment, monitoring, and performance analysis.
- Start focusing on Insurance and Advertiser Quality, with scope that broadens over time.
- Primary metric is ROAS.
Requirements
- 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
Nice to Have
- 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
Work Arrangement
Remote (Worldwide) — South Florida, remote across 18 countries
Additional Information
- Base salary is paid semi-monthly
- Future increases will be based on company and personal performance, not annual cost of living adjustments
- Compensation package includes base salary, profit-sharing bonus, and competitive benefits
