Harrison and Star

Data Scientist (ML Engineer)

Harrison and Star$120K — $140K *
Consumer Technology
5 - 7 years of experience
Job Overview by Ladders

Qualifications

  • 5+ years of experience in data science or engineering.
  • Strong understanding of algorithms and data pipelines handling large datasets.
  • Proven ability to bring concepts from initial idea to production.
  • Experience in retail media, digital advertising, or e-commerce is advantageous.
  • Familiarity with reinforcement learning and optimization techniques.
  • Curiosity about LLMs and agentic AI systems.
  • Proficient in creating and modifying traditional ML models.

Responsibilities

  • Optimize advertising strategies autonomously using data analysis.
  • Design and analyze experiments to extract actionable insights.
  • Enhance core strategies based on advertising performance data.
  • Define the interplay between machine-learning optimization and generative AI.
  • Contribute to team growth through best practice establishment and recruitment.
  • Create design documents for new initiatives.

Benefits

  • Competitive paid time off and public holidays.
  • Robust learning and development opportunities.
  • Parental leave and benefits.
  • Opportunities for volunteering.
  • Inclusive culture with employee resource groups.
Full Job Description
Agency:
Flywheel

Job Function:
Data and Analytics

Job Subfunction:
Data Engineering

Job Description:
The Opportunity

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.

Analyze advertising performance data to improve the core strategies that power Perpetua's advertising engine.

Help 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.

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 organizations with cross-functional teams of ~5 people, solving hard problems collaboratively and working tightly with your immediate team members and across the organization

Working knowledge of reinforcement learning and linear/non-linear optimization is a strong asset, given how central these techniques are to the bidding engine

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

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

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

So you know: The hired candidate will be required to complete a background check

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.

Please note,?we do not accept unsolicited resumes from 3rd party Recruitment Firms.$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.

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/

About Harrison and Star

Harrison and Star is a healthcare advertising agency that provides marketing and advertising services to pharmaceutical and biotechnology companies. The company offers a range of services including brand strategy, creative development, digital marketing, and market research. Harrison and Star was founded in 1986 and is headquartered in New York, New York.
Learn more about Harrison and Star
Size
500 employees
Industry
Founded
1986
NASDAQ

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