About Uride Technologies Inc.

Uride is a Canadian technology company redefining local transportation by building a safe, affordable, and reliable ride-sharing platform. Founded in Thunder Bay, Ontario, Uride was created to solve real problems riders and drivers face in underserved and mid-sized cities.

Today, Uride operates in 25 cities across Canada, with international expansion underway in Mexico and Germany. Our growth is driven by a clear mission: make ride-sharing more accessible while maintaining high standards of safety, reliability, and value.

Uride has been recognized by Deloitte’s Fast 50 as one of Canada’s fastest-growing technology companies, reflecting the strength of our platform, team, and execution.

We actively support community initiatives through our Good Deeds program, reinvesting in the cities we serve through local partnerships, sponsorships, and social impact efforts.

Uride is growing quickly and intentionally. Our team values ownership, speed, and accountability, and we give people the autonomy to make decisions that directly impact the business. If you want to build real products, influence strategy, and see your work matter, Uride offers that opportunity.

About the role

- Uride's first full-time Data Scientist. Reporting to the Head of Product, you'll build the predictive analytics practice from zero
- tooling, standards, and the org's first production ML systems .

- Salary range is CAD 130,000 or more (depending on experience & performance in interview)
- This role does not use AI to screen applications. Artificial intelligence is not used to make final hiring decisions; human reviewers make all decisions. However, automated interview transcription may be done to support and speed up the process




- This is a replacement role for the last manager.

What you'll do

- Set up the foundational data science stack, workflows, and standards (experimentation, model evaluation, deployment) as a reusable practice, not one-off projects
- Define and evolve the roadmap for predictive use cases across the business, in partnership with an org-wide stakeholder group (e.g., demand, churn, pricing, and fraud).
- Own models end-to-end: framing, feature engineering, training, deployment, monitoring, and retraining
- Design and run experiments (A/B tests) to validate model impact and inform product/business decisions
- Translate ambiguous business problems into measurable, tractable data science problems
- Build the metrics and measurement framework to evaluate models
- Work closely with Data Analytics on the Snowflake/dbt stack to source and shape training data
- Communicate findings and model behavior to non-technical stakeholders, including leadership
- Lay groundwork for team growth: documentation, reusable tooling, and a hiring plan as use cases scale

Must-haves

- 5+ years in data science with production ML systems shipped (not just notebooks)
- Direct experience building ML systems in some of these areas: demand forecasting, churn prediction, dynamic pricing, fraud detection & attribution
- Robust Python and SQL
- MLOps experience (deployment, monitoring, retraining)
- Comfortable building from zero with no existing team or infra
- Strong stakeholder communication, translating models into decisions

Nice to have

- Experience standing up a data science function from scratch
- Familiarity with Snowflake/dbt
- Marketplace, mobility, or two-sided platform experience
- Causal inference/experimentation design background

📌 Data Scientist (Canada)
🏢 Uride
📍 Canada