Skip to main content
Launch Potato

Staff Applied Scientist, AdTech

RemotePeru only
Published
Role
Data Science
Experience
Staff
Company size
Startup
Salary not disclosed
Check eligibility

Open to PE only. Set where you work from to check your eligibility.

No BS summary

Staff applied data scientist for adtech/performance marketing with 5+ years delivering business-impact ML. Must have expert Python and SQL, AWS ML deployment, modeling fundamentals, and hands-on experience with bandits/RL, recommendation/ranking, funnel/LTV and monetization optimization.

Core skills

AWSPythonSQL

Optional skills

Looker

What you'll do

  • 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.
  • Be 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.
  • Start focusing on Insurance and Advertiser Quality, with scope that broadens over time.
  • 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.

What they require

  • 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.
  • 5+ years in a hands-on, in-the-weeds applied data science role delivering measurable business impact.
  • 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.
  • Preferred: Sophisticated ML at companies where paid digital media is core to the business model.
  • Preferred: Creative embeddings work: incorporating embeddings of creatives, videos, headlines, and search into paid media models.
  • Preferred: Insurance domain experience.
  • Preferred: Creating state-of-the-art Ad Ranking algorithms.
  • Preferred: Modeling against ad-platform data points (Google, Meta, native).
  • Preferred: LLMs / deep learning applied to personalization or content.

Benefits

  • You own outcomes here, not tasks.
  • We measure work by impact, not activity.
  • Feedback runs direct and lands with respect.
  • Want to accelerate your career?
🇺🇸 United StatesDigital MediaStartup

Details

Apply routeGreenhouse
Salary not disclosed