Senior Data Scientist

About Bree

Bree is a consumer finance platform that brings better, faster, and cheaper financial services to over half the Canadian population who live paycheck to paycheck. We operate in a huge, but overlooked market in a country with the least amount of financial technology innovation in the developed world. Our first act is to become the cheapest and best provider of short-term credit to the 20 million people in Canada who live paycheck to paycheck.

More than half a million Canadians have already signed up with Bree, and we believe we are just scratching the surface. We are in an exciting place where we have product market fit, explosive growth, and a clear path to becoming one of the most important FinTechs in Canada.

We have $5M ARR per full-time engineer, growing at a double-digit monthly rate, profitable, and have had 0 voluntary employee churn. We were part of Y Combinator in 2021 and raised a $2M seed round shortly after.

About the Role

Bree is on a mission to build the best AI native engineering team. Ideal candidates have a deep understanding of the modern data stack and use AI tools for efficient, reliable delivery. Read more about AI native engineering teams here.

You’ll be at the intersection of product, engineering, and growth, shaping how we make decisions, measure performance, and scale data infrastructure to support millions of Canadians.

What you’ll do

• Own the experimentation strategy by designing A/B and sequential tests, setting guardrails such as power analysis, SRM checks, and CUPED, and introducing bandits when appropriate.

• Build and ship personalization, ranking, and forecasting models—recommenders, propensity, churn and LTV that measurably improve activation, conversion, and retention.

• Translate deep-dive analyses into product changes by defining success metrics and counterfactuals and partnering with Product and Engineering to deliver impact.

• Architect a modern data platform by shaping the warehouse and feature store with robust schemas, data contracts, SLAs, and governance for both operational and analytical use cases.

• Support the ML lifecycle from problem framing and data acquisition to feature pipelines, training, evaluation, deployment, monitoring, and retraining.

What you’ll need

• Expert SQL and Python with clean, modular, well-tested code and fluency across the PyData and ML stack (pandas, NumPy, scikit-learn, XGBoost or LightGBM, and deep learning as needed).

• Depth in experimentation and causal inference covering A/B design and analysis, sequential testing, CUPED, difference-in-differences, and uplift modelling with sound identification judgment.

• Strong product sense to turn ambiguity into measurable objectives, define North Star and guardrail metrics, and communicate trade-offs to executives and cross-functional partners.

• Proficiency in data visualization and BI tools such as Metabase and Streamlit, with storytelling that drives decisions rather than merely producing dashboards.

• Modern data platform and engineering rigour, including dbt and BigQuery, Snowflake, or Spark; familiarity with feature stores and real-time inference; and disciplined practices in version control, reviews, typing and tests, and secure cloud deployments.

Benefits

• Top of the market compensation for top performers

• $1,500 annual learning stipend

• $1,000 annual wellness stipend

• $250 monthly lunch stipend

• Comprehensive insurance coverage

• 2 annual company retreats

• Parental leave

• Unlimited PTO