Chorus · Toronto, Ontario
Location: Toronto, Ontario — in office ~4 days/week (68 Claremont Street) Compensation: $150,000–$170,000 CAD + equity Reports to: Technical CEO, working directly with our Founding Engineer
Chorus is marketing intelligence for nonprofits. We help mission-driven organizations send the right message to the right supporter at the right moment — building a preference profile for every supporter and routing each one to the content they're most likely to act on. Nonprofits that use Chorus raise 10-20% more from their marketing.
We're a four-person, three-founder team with a closed seed round and real go-to-market momentum. We work directly with national nonprofits and advocacy organizations, and alongside some of the field's leading agencies — reaching campaigns and programs with budgets in the billions, up to and including U.S. presidential campaigns.
This is our first dedicated data science hire.
You'll own three things that feed each other:
The studies that win clients. We land enterprise clients by running paid studies on their own marketing data and showing them the money they're leaving on the table. Your analysis is the pitch — and it converts into annual product subscriptions. You'll own these studies end to end — then present the findings yourself, alongside the founders, directly to the customer. Sometimes on site.
The models behind the product. The propensity, segmentation, and content-matching models you build get put into production as the thing clients use every day.
The story. You'll work with the founders on the data journalism and client presentations that turn a finding into a narrative people act on.
The tools underneath all of it. Our data science runs on an early internal product that automates feature engineering and optimization.
5+ years of applied data science or ML, with a track record of owning problems end to end — from a vague question through to a model in production that changed a decision.
A talent for framing. You're as sharp on deciding what to model and how to measure success as you are on the modeling itself.
Real causal inference and experimental design. Identify patterns and relationships from observational data that could uncover causal relationships.
Skeptical modeling judgment. Strong predictive-modeling chops, and — more importantly — a critical eye for evaluation. You can spot when an impressive number is an artifact of how the data was split, and you validate models in a way that reflects how they'll actually be used.
The ability to make it land. You can take a technical finding and a real trade-off and make it clear and persuasive to a client, a founder, or a non-technical fundraiser.
Comfort in the deep end. You're at home with ambiguous, fast-moving work, you set much of your own direction, and you have the judgment to know when a model is good enough to ship.
An advanced quantitative degree (statistics, economics, biostatistics, or similar). It's a strong signal for the causal side of the work — though what you've actually built matters more.
Deploying models to production and data engineering.
Marketing, CRM, or martech data experience (email engagement, supporter or donor behavior).
Uplift / heterogeneous-treatment-effect modeling — targeting who responds because of an intervention, not just who would have converted anyway.
Customer lifetime value or time-to-event (survival) modeling.
Bayesian or hierarchical modeling.
Founding Data Scientist • Toronto, Ontario, CA
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