Launch Potato

• 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. • 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.

• 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 • 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

• 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. • 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.

• 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 • Collaborate directly with stakeholders, building and validating models, and ensuring effective deployment and performance analysis • Start focusing on Insurance and Advertiser Quality, with future scope expansions • Drive lead quality improvements across various brand portfolios leading to revenue growth

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: - 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. - 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. 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. - 5+ years in a hands-on, in-the-weeds applied data science role delivering measurable business impact. Requirements - Sophisticated ML at companies where paid digital media is core to the business model (nice to have). - Creative embeddings work: incorporating embeddings of creatives, videos, headlines, and search into paid media models (nice to have). - Insurance domain experience (nice to have). - Creating state-of-the-art Ad Ranking algorithms (nice to have). - Modeling against ad-platform data points (Google, Meta, native) (nice to have). - LLMs / deep learning applied to personalization or content (nice to have). - Familiarity with Looker (nice to have). Benefits - Base salary: $175,000 to $200,000 per year, paid semi-monthly. - Compensation package includes a base salary, profit-sharing bonus, and competitive benefits. - Performance-driven company: future increases based on company and personal performance, not annual cost of living adjustments.

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. 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 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. 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 Compensation Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. Your compensation package includes a base salary, profit-sharing bonus, and competitive benefits. Launch Potato is a performance-driven company, which means once you are hired, future increases will be based on company and personal performance, not annual cost of living adjustments. Company Description Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. As The Discovery and Conversion Company, our mission is to connect consumers with the world’s leading brands through data-driven content and technology. Headquartered in South Florida with a remote-first team spanning over 15 countries, we’ve built a high-growth, high-performance culture where speed, ownership, and measurable impact drive success.

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. 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 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 - 5+ years in a hands-on, in-the-weeds applied data science role delivering measurable business impact Requirements - Sophisticated ML at companies where paid digital media is core to the business model (nice to have) - Creative embeddings work: incorporating embeddings of creatives, videos, headlines, and search into paid media models (nice to have) - Insurance domain experience (nice to have) - Creating state-of-the-art Ad Ranking algorithms (nice to have) - Modeling against ad-platform data points (Google, Meta, native) (nice to have) - LLMs / deep learning applied to personalization or content (nice to have) - Familiarity with Looker (nice to have) Benefits - Base salary: $175,000 to $200,000 per year, paid semi-monthly - Compensation package includes a base salary, profit-sharing bonus, and competitive benefits - Performance-driven company: future increases based on company and personal performance

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. 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 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. 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 Benefits - Base salary: $175,000 to $200,000 per year, paid semi-monthly - Compensation package includes a base salary, profit-sharing bonus, and competitive benefits - Performance-driven company: future increases based on company and personal performance

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. 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 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. 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 Benefits - Base salary: $175,000 to $200,000 per year, paid semi-monthly - Compensation package includes a base salary, profit-sharing bonus, and competitive benefits - Future increases based on company and personal performance, not annual cost of living adjustments

• Lead & Execute programs to reduce inventory and controllable losses across assigned locations • Investigate internal and external theft, bringing cases to successful resolution • Audit & Analyze store procedures using exception-based reporting and compliance reviews • Partner & Communicate with store, district, regional, and corporate teams to drive organizational goals • Optimize Security Controls to prevent shrinkage and ensure associate safety • Manage Prosecution & Restitution procedures to maximize recovery for the company