About this role.
This Staff Data Scientist role owns end-to-end applied machine-learning work for a marketing and insurance vertical, with ROAS as the central business metric. The position requires hands-on development of production-oriented models for acquisition, lead quality, monetization, ranking, recommendations, and LTV optimization. The successful candidate will partner directly with business stakeholders and ML engineers, remaining accountable from problem framing through model monitoring and performance analysis. Strong expertise in Python, SQL, AWS, performance marketing, and measurable commercial outcomes is essential. The role is remote across specified Canadian locations and emphasizes high ownership, direct communication, and rapid iteration.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
5/5Autonomy Level
5/5Communication Load
5/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
I would first define the optimization unit, decision point, attribution window, and ROAS formula with stakeholders. I would then establish a baseline, audit conversion and revenue-label quality, account for delays and selection bias, and evaluate the model through offline metrics plus a controlled online experiment measuring incremental ROAS and lead quality.
I would define each eligible targeting, creative, or bidding policy as an arm and choose a reward aligned to contribution profit or quality-adjusted revenue rather than clicks alone. I would begin with conservative exploration, include guardrails for spend and quality, handle delayed conversions where necessary, and compare the policy against the existing allocation strategy through holdout testing.
I validate data lineage, leakage risk, feature availability, calibration, segment performance, and expected business impact before handoff. I document assumptions and decision thresholds, partner with ML engineering on serving and monitoring requirements, and define alerts for data drift, prediction drift, model quality, and core business metrics such as ROAS.
I would create labels that reflect mature downstream value while explicitly accounting for censoring and conversion delay. Depending on the data, I would use survival methods, delayed-feedback adjustments, or proxy labels calibrated to eventual value, then continuously refresh the model as additional outcomes mature.
I focus on the business decision, expected financial impact, confidence level, trade-offs, and recommended next action rather than algorithmic detail. I use clear visuals and plain language, explain relevant assumptions and risks, and establish an experiment or rollout plan that lets stakeholders evaluate results against agreed success metrics.
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.
MUST HAVE:
- 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
EXPERIENCE: 5+ years in a hands-on, in-the-weeds applied data science role delivering measurable business impact.
YOUR ROLE
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
COMPETENCIES
- 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
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
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.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
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