launch2
Staff Applied Scientist, AdTech
At a Glance
- Location
- Chicago, IL (remote); Columbus, OH (remote); Detroit, MI (remote); Kansas City, KS (remote); Madison, WI (remote); Saint Louis, MO (remote)
- Work Regime
- remote
- Experience
- 5+ years
- Compensation
- high-performers. BASE SALARY: $175,000 to $200,000 per year, paid semi-monthly M
- Posted
- 2026-07-29T09:21:50-04:00
Key Requirements
Required Skills
Domain Knowledge
- Engineering
- Insurance
- Media
Requirements
5+ years in a hands-on, in-the-weeds applied data science role delivering measurable business impact.
Business-first framing:
Full-stack ownership:
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
Compensation & Benefits
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.
Responsibilities
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.