{"id":1493598,"url":"https://alion.io/job/omnicom-data-scientist-ml-engineer","title":"Data Scientist (ML Engineer)","company":{"id":720301,"name":"Omnicom","domain":"omc.com","url":"https://alion.io/company/omnicom","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Toronto, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":73000,"max_usd":163000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":70},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"BigQuery","optional":false},{"name":"Claude Code","optional":false},{"name":"GCP","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Vertex AI","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-07-28T00:00:00Z","employer_posted_date":"2026-07-28","last_verified_at":"2026-09-30T01:21:57Z","board_verified":true,"closed_at":null,"days_open":65,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":65},"description":"Agency:\nFlywheelJob Function:\nData and AnalyticsJob Subfunction:\nData EngineeringJob Description:\nAbout FlywheelFlywheel’s suite of digital commerce solutions accelerate growth across all major digital marketplaces for the world’s leading brands. We give clients access to near real-time performance measurement and improve sales, share, and profit. With teams across the Americas, Europe and APAC, we offer a career with real impact, endless growth opportunities and the support you need to be the best you can be.\nThe Opportunity\nPerpetua 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.\nAs 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.\nOur 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.\nWhat You Will Do:\nWork 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.\nDesign, Implement, and Analyze experiments for deriving Actionable Insights.\nAnalyze advertising performance data to improve the core strategies that power Perpetua's advertising engine.\nHelp define how Perpetua’s machine-learning optimization works alongside the emerging generative and agentic layer, 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.\nSupport the growth of the team by contributing to activities for establishing best practices, recruitment, and authoring design documents.\nWho You Are:\n5+ years of experience as a data scientist or engineer working with data scientists\nStrong experience with algorithms and data pipelines processing terabytes of data per day\nYou have experience taking concepts from inception through to production and ongoing monitoring and enhancements\nExperience in retail media, digital advertising, or e-commerce is an asset\nYou have worked in organizations with cross-functional teams of ~5 people, solving hard problems collaboratively and working tightly with your immediate team members and across the organization\nWorking knowledge of reinforcement learning and linear/non-linear optimization is a strong asset, given how central these techniques are to the bidding engine\nCuriosity about applied LLMs and agentic systems, and comfort using modern AI-assisted development tools (such as Claude Code) as part of how you build\nAble to create and make changes to traditional ML models, including but not limited to Linear regression, XGBoost and Logistic regression\nCompetent in training and evaluating models using mainstream data science tools including but not limited to sklearn, Pandas, keras and/or PyTorch\nExperience in cloud native ML training platforms like BigQuery ML or Snowpark\nWorking at Flywheel\nWe are proud to offer all Flywheelers a competitive rewards package and unparalleled career growth opportunities and a supportive, fun and engaging culture.\nWe 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\nCompetitive paid time off, including annual leave plus paid public holidays\nGreat learning and development opportunities\nBenefits that help you live your best life\nParental leave and benefits\nVolunteering opportunities\nIf you’re looking to connect with teammates on a topic of inclusion and identity, chances are there’s an ERG for that.\nSo you know: The hired candidate will be required to complete a background check\nLearn more about us here: Life at Flywheel\nThe Interview Process:\nEvery 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.\nEvery 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.\nInclusive Workforce\nFlywheel 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.\nIf you have any accessibility requirements that would make you more comfortable during the application and interview process, please let us know at  so that we can support you.\nFor more information about what data we collect and how we use it, please refer to our Privacy Policy.\nWe leverage AI technology to streamline our hiring workflow, though all candidate decisions are made by our Talent Acquisition Team. This position is for an existing vacancy.\nIMPORTANT ALERT: Please beware of fraudulent job communications from individuals falsely claiming to be from Flywheel. We've identified fraudulent activity through social media and messaging services purporting to be from Flywheel requesting payments for job- and recruitment-related expenses. Flywheel never asks candidates for personal information such as bank account data or tax IDs nor payments via social media or chat-based applications. Report suspected fraud to local authorities immediately. To learn more, click here.\nPlease note,?we do not accept unsolicited resumes from 3rd party Recruitment Firms.\n\n$120,000 - $140,000 CAD\nOmnicom’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.\nOmnicom 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.\nLink to Recruitment Privacy Notice: https://www.omc.com/privacy-notice/","description_format":"text","description_chars":8401,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Annual leave","Growth opportunities","Parental leave"],"hiring_locations":[{"name":"Canada","iso":"CA","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Commerce","Multimodal AI"],"lifecycle":[{"event":"open","at":"2026-09-30T01:21:57Z"}],"liveness":{"score":12,"band":"cold","label":"Long shot","p_open":1,"p_active":0.424,"p_room":0.28,"age_days":65,"expected_fill_days":15,"reasons":["conf:28","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-01T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/omnicom-data-scientist-ml-engineer","json_url":"https://alion.io/job/omnicom-data-scientist-ml-engineer.json","meta":{"generated_at":"2026-10-01T11:42:05Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":3435,"day_limit":5000,"remaining_today":1565,"minute_limit":60,"resets_at":"2026-10-02T00:00:00Z"}}}