Staff Data Scientist, Lead Generation
我们是谁？发起豆是一个发现和转换公司。我们通过我们的品牌每月接触到数以千计的人，包括 FinanzBuzz,All About Cookies 和 OnlyInYourState. 我们把这种关注转化为全球领先的合作伙伴的一些公司的实际结果。我们的使命：通过将发现，意图和业绩连接到金融，旅游和娱乐领域的顶级消费品牌中，在规模上创造可预测的高价值客户收购。我们运行营得利且启动，拥有南佛罗里达总部和一个远程第一团队18个国家。为什么加入我们？你在这里拥有结果，而不是任务。我们衡量工作的影响，而非活动。速度是真实的，变化是恒定的，所以最好的人才把变化变成机会而不是等待它解决。反直接运行并与尊重地降落。普通的人在发起土豆时会让自己成为非凡的。你的角色 你将建立模型来决定每个汽车保险购物者看到我们的单击墙上的航母。目标是向每一个用户送到最有可能转换的航母，这样运营商可以看到我们的流量转换和提高他们的报价。你还拥有我们媒体背后的购买模式，这些投标被托付给 ROAS. 运营商转换数据 （报价，政策和收购成本）现在回来了我们这里，你将是把它放在生产中工作的人。最难的是数据。结果在点击后数周或几个月内出现，许多会议很少，同样的买家返回，市场漂移。我们的投标也决定我们赢得哪些用户，所以模型塑造了从下一个学习的数据。这是一个实践中的个人贡献者角色。你直接与业务构建问题，找到自己的数据，建立和验证模型，并通过部署和监测保持它。ML 工程师在生产方面与你一起工作。汽车保险需要大部分时间才能开始，而且随着时间的推移范围扩大到其他垂直领域。我们是远程先，大多数公司工作东海岸小时。到60日，你将自行运营我们的保险市场媒体购买。在你的第一年底之前：· 点击墙面服务，每个用户与正确的运营商相匹配 · 运营商转换数据在生产模型中运行 · 自动化市场竞标从概念证明到规模 · 对重复用户和稀缺会议进行清晰方法 · 产品模式能承受困扰
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WHO ARE WE?
Launch Potato is the discovery and conversion company. We reach tens of millions of people every month through our brands, including FinanceBuzz, All About Cookies, and OnlyInYourState. We turn that attention into real outcomes for our partners, some of the world's leading companies.
Our mission: create predictable, high-value customer acquisition at scale by connecting discovery, intent, and performance across category-leading consumer brands in finance, travel, and entertainment.
We run profitable and bootstrapped, with a South Florida HQ and a remote-first team across 18 countries.
WHY JOIN US?
You own outcomes here, not tasks. We measure work by impact, not activity. The pace is real, and change is constant, so the people who do best turn change into opportunity instead of waiting for it to settle. Feedback runs direct and lands with respect. Ordinary people make themselves extraordinary at Launch Potato.
YOUR ROLE
You'll build the models that decide which carrier each auto insurance shopper sees on our click wall. The goal is to send every user to the carrier most likely to convert them, so carriers see our traffic convert and raise their bids. You'll also own the buying models behind our media spend, which are held to ROAS.
Carrier conversion data (quotes, policies and acquisition cost) is now coming back to us, and you'll be the one who puts it to work in production. The hard part is the data. Outcomes land weeks or months after the click, many sessions are sparse, the same shoppers come back, and the market drifts. Our bids also decide which users we win, so the model shapes the data it learns from next.
This is a hands-on individual contributor role. You frame problems directly with the business, find your own data, build and validate the model, and stay with it through deployment and monitoring. An ML engineer works alongside you on the production side. Auto insurance takes most of your time to start, and scope broadens to other verticals over time.
We're remote first, and most of the company works East Coast hours.
WHAT SUCCESS LOOKS LIKE
By day 60, you run our insurance marketplace media buying on your own.
By the end of your first year:
- Click wall serving that matches each user to the right carrier
- Carrier conversion data running in production models
- Automated marketplace bidding taken from proof of concept to scale
- A clear approach for repeat users and sparse sessions
- Production models that hold up through drift and changing objectives
- Output the business trusts and uses without rework
MUST HAVE
- 5+ years of hands-on applied data science with measurable business impact
- Your models drove paid media decisions: spend, bidding or ad serving. Attribution and reporting alone don't qualify.
- You built what's on your resume and can explain it down to the loss function and architecture choice, neural network fundamentals included
- You see how today's bids shape tomorrow's training data, and you've handled late labels, sparse features or drift
- You own models end to end: you find your own data, build through deployment and monitoring, and bring a point of view
- Expert Python and SQL
NICE TO HAVES
- Real ad budget spent in live auctions
- Large-scale ranking or recommendation systems
- Explaining tradeoffs to non-technical partners in plain terms
- Designing for auction feedback loops, selection bias and exploration
- Hands-on neural network work, such as embeddings of text, creative or user behavior
- AWS for data science: ECS, S3, Athena, SageMaker
- CI/CD deployment
TOTAL 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.
Want to accelerate your career? Apply now!
Since day one, we've been committed to having a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company. We value diversity, equity, and inclusion.
We do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.