Senior Growth Data Scientist
New
Remote-first teamFull-TimeSenior
Salary150,000 CAD - 195,000 USD per year
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Job Details
- Experience
- 5 years
- Required Skills
- PythonSQLTableauData scienceLookerR
Requirements
- 5 years in marketing/growth analytics or data science, ideally at a consumer-facing tech, fintech, or startup.
- Advanced SQL and comfort working with large-scale data in modern warehouse environments.
- Proficiency in Python or R for analysis and modeling.
- Strong grounding in marketing attribution, experimentation and statistics, funnel analysis, retention, ARPU, and LTV.
- Hands-on experience with mobile measurement platforms (AppsFlyer preferred): SDK/event setup and attribution modeling.
- Proficiency with BI/visualization tools (Tableau, Looker, or similar).
- Excellent communication and the ability to influence non-technical stakeholders.
- A self-starter comfortable building from scratch in an ambiguous, fast-moving environment.
- Bachelor's in a quantitative field (Master's a plus).
- Crypto, fintech, or financial services experience is a plus.
- Experience with ML applied to marketing (propensity, churn, LTV prediction, audience segmentation) is a plus.
Responsibilities
- Serve as the embedded data science partner to Marketing, bringing rigorous, data-driven decision-making to a team that today lacks dedicated analytical support.
- Stand up and own the marketing measurement foundation — event tracking architecture, unified attribution windows, and KPI frameworks across mobile (MMP) and web (GA) — so channels can be compared apples-to-apples.
- Build and maintain the core models that drive spend decisions: LTV, ROAS, CAC/payback, and retention; translate them into channel strategy and budget allocation recommendations.
- Design, run, and read out experiments and A/B tests across acquisition and the customer lifecycle, and set the experimentation standard for the marketing org.
- Analyze the full funnel (acquisition → activation → engagement → retention → monetization), surface the highest-impact growth opportunities, and size them.
- Build forecasting and opportunity-sizing models to inform planning and resource allocation.
- Develop self-serve dashboards and recurring reporting that give Marketing and leadership clear visibility into performance.
- Partner with Data Engineering and MMP/ad-platform teams to ensure data quality and measurement accuracy, and prioritize fixes.
- Translate analysis into clear, persuasive recommendations, and influence marketing investment decisions with stakeholders up to leadership.
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