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Launch Potato

Staff Applied Scientist, AdTech

RemoteUnited States, Chile only
Published
Role
Data Science
Experience
Staff
Company size
Mid-size
$175k–$200k/yr
Check eligibility

Open to US, CL only. Set where you work from to check your eligibility.

No BS summary

Staff-level applied data scientist for AdTech with 5+ years hands-on applied data science experience. Must have Python, SQL, AWS ML deployment, strong modeling fundamentals, and experience in digital/performance marketing or leadgen. Role is remote but limited to listed US metro locations.

Core skills

PythonSQLRecommendation systems

Required skills

AWSMulti-armed banditReinforcement learningRanking systemsLTV modeling

Optional skills

EmbeddingsGoogleMetaLLMsDeep learningLooker

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.
  • Frame the business problem directly with stakeholders.
  • Build and validate the model.
  • Hand the ML-engineering last mile to your ML engineering partner.
  • Stay 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 the 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 is 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.
  • Preferred: Familiarity with Looker.

Benefits

  • Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework.
  • Compensation package includes a base salary, profit-sharing bonus, and competitive benefits.
  • Future increases will be based on company and personal performance, not annual cost of living adjustments.
🇺🇸 United StatesDigital MediaStartup

Details

Apply routeGreenhouse
$175k–$200k/yr