Staff Data Scientist
At Roofr , we’re obsessed with our customers. We constantly gather feedback to shape, prioritize, and launch the products they truly need. That’s what makes Roofr ’s CRM special. We started by building essential sales tools like aerial roof measurements and digital sales proposals. But when our customers asked for a simple, affordable way to manage and scale their entire businesses, we listened. So, we created a CRM that connects these solutions—along with payments, material ordering, and more—into a seamless, powerful platform. With a clear roadmap ahead, we’re excited to continue expanding and leading the market with innovative products.
We have an amazing culture, strong financials, and best-in-class company metrics. It’s an exciting time to be part of an extraordinary startup that is already successful, yet still early enough to offer its team significant growth, equity, and the opportunity to make a real impact.
This position is for an existing vacancy.
As a Staff Data Scientist at an early-stage, high-growth company, you'll play a critical role in turning raw data into insights and apps that shape strategy, fuel growth, and improve the customer experience. You'll have a direct hand in the decisions that move the business, not just the reports that describe it.
This isn't a "run the numbers and hand off a deck" role. You'll be partnering directly with senior leaders and an experienced data & analytics team to drive data science and experimentation initiatives across sales, marketing, and product areas — putting both traditional analysis and ML to effect change.
What You'll Get to Do:
- Architect optimization analyses and forecasts that help leaders pinpoint opportunities across Growth, Strategy, Revenue, Marketing, and Product teams.
- Partner directly with executive leadership, translating complex analysis into clear, actionable decisions and process changes.
- Design counterfactual analyses and A/B tests to enable data-driven evolution of our UX, product features and customer experience.
- Build data apps that automate and supercharge our sales and marketing processes.
- Own your projects end-to-end — from framing the business question to shipping the model to presenting the recommendation, with the autonomy to decide how you get there.
- Work on top the same semantic layer and DBT models powering our AI-driven analytics stack (Snowflake Cortex, Sigma, Claude, etc.), so your analyses build on a single-source-of-truth data.
- Create data science frameworks — templates and guides that data practitioners across the org can leverage and build upon.
What You'll Bring to the Role:
- 7+ years of experience in data science in a fast-moving business setting, with a track record of running the show on ambiguous, high-stakes problems.
- Hands-on experience with a modern cloud data stack — Snowflake, DBT, and various BI tools (Sigma, Looker, Metabase, Omni, or similar).
- Strong SQL and statistical modeling skills, with a track record of building forecasting and causal inference models.
- A love of documentation and Git — you enable others to build on top of your work..
- You appreciate scalable data models — you contribute to the shared semantic layer, and let the whole org benefit.
- You're just as excited to hack together a scrappy new analysis or app as you are to build scalable products that meet stringent quality standards.
Who You Are:
- Self-driven and organized, with the ability to manage projects across cross-functional stakeholder groups with minimal oversight, keeping everyone updated on progress and next steps.
- You don't wait for direction — you spot the opportunity, make the case, and drive it through.
- An amazing communicator and presenter, equally comfortable in a room with executive leadership or heads-down in the data.
- A true team player — you love brainstorming, peer reviews, and pressure-testing ideas.
- You're in it for the journey, not just the destination.
Compensation Band:
This role requires you to be in Canada. If that means relocating or flying in, it is worth checking fares before you commit to a start date.
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