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Toronto, Ontario, Canada
Actively hiring, first seen by us 6 days agoEmployment type in the employer’s posting: Full timeFrom the employer’s posting
Build optimization systems that set bids and place spend autonomously for advertisers
Design and analyze experiments to derive actionable insights
Analyze advertising performance data to improve core bidding strategies
Decide where reinforcement learning, classical optimization, or LLM reasoning fit
the system that sets bids and allocates spend in production, in real time, against each advertiser’s goals
Design, Implement, and Analyze experiments for deriving Actionable Insights.
Analyze advertising performance data to improve the core strategies that power Perpetua's advertising engine.
deciding where reinforcement learning and classical optimization are the right tools, and where LLM-based reasoning meaningfully improves how the platform plans and explains its decisions
From the employer’s posting.
Flywheel’s suite of digital commerce solutions accelerate growth across all major digital marketplaces for the world’s leading brands.
In the employer’s words · Read in context
Actively hiringFirst seen by us 6 days ago, still listed, and not a repost of an earlier posting.
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
Day 6
Similar jobs usually come down between day 6 and day 69.
From 4,296 similar postings that came down. Coming down is not always a hire.
150 open jobs
Open jobs by the week we first saw them, last 12 weeks.
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Perpetua is the retail media platform within the Flywheel Commerce Network, built for the challenger brand; the operator who cannot out spend the category leader and has to out execute instead. Advertisers set goals based on strategy and Perpetua’s always on optimization executes the tactics. 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 optimize advertising autonomously on the customer’s behalf. Design, Implement, and Analyze experiments for deriving Actionable Insights.
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
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
Curiosity about applied LLMs and agentic systems, and comfort using modern 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
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We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Agency:
Flywheel
Job Function:
Data and Analytics
Job Subfunction:
Data EngineeringJob Description:
About Flywheel
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.
Omnicom is committed to hiring and developing exceptional talent. We agree that talent is uniquely distributed, and we’re focused on developing inclusive teams that can bring the best solutions to everything we do. We strongly believe that celebrating what makes us different makes us better together. Join us—we look forward to getting to know you. We will process your personal data in accordance with our Recruitment Privacy Notice.
Link to Recruitment Privacy Notice: https://www.omc.com/privacy-notice/
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Toronto, Ontario, Canada
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