As a Data Scientist (ML Engineer), on the Perpetua team, you will design, experiment with, and ship the machine learning systems that decide how thousands of brands spend their advertising budgets across retail media.

This is the engine that takes autonomous action on the customer’s behalf.

It is not a model that produces recommendations for someone else to act on, but the system that sets bids and allocates spend in production, in real time, against each advertiser’s goals.

Your work runs live across thousands of customers worldwide.

Our team primarily works with Python and the Google Cloud Platform suite of products like Cloud Run and Vertex AI to productize cutting‑edge data features.

We are currently working on developing a scalable advertising bidding platform that enables advertisers to implement custom and versatile bidding strategies including but not restricted to maximizing advertising sales, dominating top‑of‑search placements, optimizing for total sales, incremental sales, new‑to‑brand purchases, organic rank, etc.

Increasingly, this work sits alongside a newer layer of generative and agentic AI; LLM‑based reasoning that plans, explains, and reacts to natural language goals.

Knowing where classical optimization is the right tool and where the generative layer adds leverage is part of the craft on this team.

What You Will Do:

- Work across retail media (starting with Amazon) to understand the intricate relationships between bids, placement, conversion, and sales, and turn that understanding into systems that optimise advertising autonomously on the customer’s behalf.
- Design, Implement, and Analyse experiments for deriving Actionable Insights.
- Analyze advertising performance data to improve the core strategies that power Perpetua's advertising engine.
- Help define how Perpetua’s machine‑learning optimisation works alongside the emerging generative and agentic layer, deciding where reinforcement learning and classical optimisation are the right tools, and where LLM‑based reasoning meaningfully improves how the platform plans and explains its decisions.
- Support the growth of the team by contributing to activities for establishing best practices,



recruitment, and authoring design documents.

Who You Are:

- 5+ years of experience as a data scientist or engineer working with data scientists
- Strong experience with algorithms and data pipelines processing terabytes of data per day
- You have experience taking concepts from inception through to production and ongoing monitoring and enhancements
- Experience in retail media, digital advertising, or e‑commerce is an asset
- You have worked in organisations with cross‑functional teams of ~5 people, solving hard problems collaboratively and working tightly with your immediate team members and across the organisation
- Working knowledge of reinforcement learning and linear/non‑linear optimisation is a strong asset, given how central these techniques are to the bidding engine
- Curiosity about applied LLMs and agentic systems, and comfort using up-to-date AI‑assisted development tools (such as Claude Code) as part of how you build
- Able to create and make changes to traditional ML models, including but not limited to Linear regression, XGBoost and Logistic regression
- Competent in training and evaluating models using mainstream data science tools including but not limited to sklearn, Pandas, Keras and/or PyTorch
- Experience in cloud native ML training platforms like BigQuery ML or Snowpark

Working at Flywheel

We are proud to offer all Flywheelers a competitive rewards package and unparalleled career growth opportunities and a supportive, fun and engaging culture.

We have office hubs across the globe where team members can go to feel productive, inspired, and connected to others – team members go into Hub Offices 3x a week.

- Competitive paid time off, including annual leave plus paid public holidays
- Great learning and development opportunities




- Benefits that help you live your best life
- Parental leave and benefits
- Volunteering opportunities

Flywheel Commerce Network’s goal is to create a culture where all individuals of all backgrounds feel comfortable in bringing their authentic selves to work.

We want all people to feel included and empowered to contribute fully to our vision and goals.

Flywheel Commerce Network is an Equal Opportunity Employer and participates in E-Verify. All applicants will receive fair consideration for employment.

We do not discriminate based upon race, color, religion, sex, sexual orientation, age, marital status, gender identity, national origin, disability, or any other applicable legally protected characteristics in the location in which the candidate is applying.

If you’re looking to connect with teammates on a topic of inclusion and identity, chances are there’s an ERG for that.

$120,000 - $140,000 CAD

Omnicom’s policy requires employees to work in the office for a minimum of three days a week, unless additional in‑office days are directed by their agency or manager.

Our objective is to increase this requirement over time, and many of our agencies as well as Omnicom’s corporate group already require five days of in‑office attendance.

This position is for an existing vacancy.

We will process your personal data in accordance with our Recruitment Privacy Notice.

Link to Recruitment Privacy Notice:

Learn more about us here: Life at Flywheel

The interview process: Every role starts the same, an introductory call with someone from our Talent Acquisition team. We will be looking for company and values‑fit as well as your professional experience; there may be some technical role-specific questions during this call. Every role is different after the initial call, but you can expect to meet several people from the team 1:1 and there might be further skill assessments in the form of a Take Home Assignment/Case Study Presentation or Pair Programming/Live Coding exercise depending on the role. In your initial call, we will walk you through exactly what to expect the process to be.

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📌 Data Scientist (ML Engineer) (Toronto)
🏢 Perpetua
📍 Toronto