Machine Learning Engineer, AI Decisioning

HRB; Canada; Remote; CAD 80,000 - 120,000; Full time

Job summary

A data activation company based in Canada is looking for a machine learning engineer to build solutions that enhance customer interaction through personalized recommendations and predictive modeling. This role involves working on automation and using customer data to create a powerful intelligence layer. The ideal candidate will have experience in machine learning, data analysis, and managing diverse tasks from customer research to model development.

Qualifications

• Experience in building machine learning models.
• Strong understanding of data activation products.
• Ability to work with customer data for intelligence layer.

Responsibilities

• Build comprehensive machine learning solutions.
• Work on customer research and problem definitions.
• Develop predictive models and machine learning infrastructure.

Skills

Machine Learning

Predictive Modeling

Data Analysis

Natural Language Processing

Experimentation Automation

Job description

About the Role

We’re looking to hire amachine learning engineeras we expand our data activation products to include an intelligence layer. While hundreds of companies use our products today to sync data into their SaaS systems to automate and improve operations, there’s a lot of surface area we haven’t touched in helping companies figuring out which customers to message, what content to put in messages, and when to send messages. A lot of this work today is done manually through intuition and guesswork, and we believe that adding machine learning could have a step function impact for our customers. And given our access to data warehouses and databases, we are perfectly placed to make use of a company’s customer data in building a powerful intelligence layer.

Some of the problems we’ll be working on include:

• Personalization and Product Recommendation: There are often many options for what content a company could message a user with, including which products to show from catalogues. Given this large state space, how can we help personalize messages with the most relevant content for each user?
• Automated Experimentation: Helping companies intelligently navigate and automate experiments across the extensive number of options for messaging customers.
• Predictive Audiences: Building models to predict which users are most likely to convert, churn, or take desired actions.
• Content Generation: Particularly with recent advances in LLMs, how can we help marketers generate text, images, and creatives that are compelling to their customers?
• Budget Optimization: Helping companies assess which marketing spend is driving the mostincrementalconversions, and where themarginalCAC is lowest.

As an early machine learning engineer, you will help build comprehensive solutions to the above domains from scratch. Responsibilities will be highly varied and include working on customer research, problem definition, predictive modeling, machine learning infrastructure, and partnering with customers.