Senior Data Scientist - ML Bidding & Ad Tech $200K in Columbus
Career & Market Insight
Compensation & Market Positioning: At $175,000–$200,000 base, this role sits at the 75th–90th percentile for Senior/Staff Data Scientists in the US, particularly strong for a non-FAANG company. The profit-sharing bonus adds meaningful upside. Note that future raises are performance-based, not cost-of-living — high performers benefit, but this can lag inflation for average performers.
Skills You'll Build: This role develops a highly marketable, niche skillset at the intersection of ML and performance marketing:
- Causal inference and feedback loop modeling (bids shaping training data)
- Real-time auction systems and ROAS optimization
- End-to-end ML ownership: data sourcing through production monitoring
- Delayed/sparse label handling — a rare and in-demand expertise
- AWS ML stack (SageMaker, Athena, ECS)
Career Trajectory: This is an individual contributor (IC) role with no explicit management track mentioned — ideal for deep technical specialists. The scope expansion to "other verticals" signals potential for broader platform ownership over time.
Company Signals: Bootstrapped and profitable is a strong green flag — no VC pressure, no sudden layoffs chasing runway. Operating across 18 countries suggests a mature remote culture. The 5+ year experience bar and emphasis on owning models end-to-end indicate they want a senior practitioner, not a researcher.
Green Flag: Direct business impact framing and working alongside an ML engineer means you can stay focused on modeling rather than DevOps. Potential watch point: The "change is constant" language warrants clarifying what stability looks like for long-running model projects.
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.
BASE SALARY: $175,000 to $200,000 per year, paid semi-monthly
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.
Frequently Asked Questions
Is this role fully remote or is there an office requirement?
What is the salary range for this Data Scientist position?
What kind of ML experience is required — research or applied?
Will I manage a team or is this an individual contributor role?
Does the scope of this role expand beyond auto insurance?
