Lead Fullstack Data Scientist, TILT

• Exceptional analytical and technical ability with a track record of high-impact work in full-stack data science or analytics
• Proven experience building and leading high-performing data science teams
• Deep expertise in experimentation, statistical modelling, causal inference, and translating complex outputs into clear strategy
• Strong engineering ability across the data stack, comfortable owning systems end-to-end
• High bias for action and speed, with excellent product and business intuition
• Experience in fast-paced, high-autonomy startup environments, ideally from seed to Series B
• First-principles thinker with strong problem-solving instincts

Desirable

• We are building the intelligence layer that powers every major decision at Tilt
• As our Lead Full Stack Data Scientist, you will architect and own this system end to end, from data infrastructure and pipelines to modelling, experimentation, and analytics
• You will build and lead the team responsible for turning Tilt’s data into high-leverage product, growth, and commercial insights
• This role is both strategic and deeply hands-on
• You will define what data science means at Tilt, set the long-term direction, and ship systems that unlock instant insight across the company
• In one recent project, we cut the time to insight from days to minutes
• You will deliver this level of impact at scale
• 0 to 3 months
• Map Tilt’s business model, data ecosystem, and analytical gaps
• Define and begin executing the data science roadmap aligned with company priorities
• Build high-impact analytics for Marketing, Creator, and Category teams that deliver immediate value
• Establish data quality standards, governance, and best practices across all functions
• Embed fast, scientific decision-making into the company by partnering with senior leadership
• 3+ months
• Own and evolve Tilt’s entire data science, modelling, and analytics strategy
• Lead frameworks for marketing efficiency, attribution, and smart budget allocation
• Design, build, and maintain predictive models that support category expansion, retention, creator performance, and growth
• Develop and operationalise causal inference, experimentation, and incrementality testing systems
• Establish a company-wide culture of experimentation and rigorous measurement
• Represent data science in leadership and board-level discussions