Canada Trade Promo Optimization (TPO) transformation: modernizing the end-to-end pipeline - ON-1064

Project type: Innovation
Desired discipline(s): Engineering - computer / electrical, Engineering, Computer science, Mathematical Sciences, Statistics / Actuarial sciences
Company: Unilever Canada
Project Length: 6 months to 1 year
Preferred start date: 04/01/2025
Language requirement: English
Location(s): Toronto, ON, Canada
No. of positions: 2
Desired education level: Undergraduate/BachelorMaster'sPhDPostdoctoral fellowRecent graduate
Open to applicants registered at an institution outside of Canada: No

About the company: 

Unilever Canada is a leading consumer goods company that meets everyday needs for nutrition, hygiene, and personal care with a wide range of trusted brands. Located in Toronto, Ontario, Unilever Canada is dedicated to improving the quality of life for its consumers by providing products that help people feel good, look good, and get more out of life. The company is committed to sustainability and innovation, striving to make a positive impact on the environment and society.

Describe the project.: 

Trade Promo Optimization (TPO) is a Machine Learning tool developed by Unilever for promotional strategic planning. The initiative starts years ago and the team has been successfully bringing the tool from a PoC to a crucial tool used by several business stakeholders. As the scope is getting larger, we’d like to revisit the end-to-end workflow and modernize it to meet the industry standard, including but not limited to refine the data refreshment pipeline, automate the feature store generation, refactor and package the Machine Learning pipeline as well as the optimization pipeline, and exploring new techniques like MLOps.

The candidate will be working with retailer point-of-sale data, micro-economics data, macro-economics data, and any other retailer specific data.

Required expertise/skills: 

Machine Learning, Optimization, Software Development, Design Patterns, MLOps, Data Engineer