Senior Machine Learning Engineer Job Mississauga area,Ontario Canada,IT/Tech

CHEP helps move more goods to more people, in more places than any other organization on earth via our 347 million pallets, crates and containers. We employ approximately 13,000 people and operate in 60 countries. Through our pioneering and sustainable share-and-reuse business model, the world’s biggest brands trust us to help them transport their goods more efficiently, safely and with less environmental impact.

What does that mean for you? You’ll join an international organization big enough to take you anywhere, and small enough to get you there sooner. You’ll help change how goods get to market and contribute to global sustainability. You’ll be empowered to bring your authentic self to work and be surrounded by diverse and driven professionals. And you can maximize your work-life balance and flexibility through our .

Job Description

Key Responsibilities May Include:

Collaborate with key stakeholders to identify business challenges, translating ambiguous problems into structured analyses using statistical modelling and machine learning algorithms.

Lead the selection, validation, and optimization of models to discover meaningful patterns and insights, ensuring models remain relevant, reliable, and scalable.

Drive continuous integration and deployment of data science solutions, optimizing performance through advanced machine learning techniques, code reviews, and best practices.

'Develop and deliver sophisticated visualizations, dashboards, and reports translate complex data into clear, actionable insights for business stakeholders.

Present technical solutions to business stakeholders, using creative methods to explain complex concepts, increase understanding, and encourage solution adoption.

Mentor and develop junior data scientists, fostering a culture of continuous learning, knowledge sharing, and skills development within the organization.

Write clean, high-quality code, ensuring all outputs pass quality assurance checks, and contribute to the development of novel solutions to solve complex business problems.

Stay informed on industry trends, emerging tools, and techniques, applying them to improve data science practices and encourage innovation within the team.

Lead strategy development for one or more data products, managing roadmaps, identifying requirements, and collaborating with business stakeholders to ensure alignment with business goals.

POSITION PURPOSE

We are seeking a Senior Machine Learning Engineer to design, build, deploy, and operate scalable machine learning and AI solutions in production. This role sits at the intersection of MLOps, traditional data science modeling, and software engineering, with opportunities to work on AI/GenAI engineering use cases.

You will work closely with Data Scientists and Engineers to productionize ML and emerging GenAI solutions, owning the full lifecycle from model development through deployment, monitoring, and iteration.

SCOPE

• Machine Learning models for Advanced D&A Americas.

• Data products initiatives for Advanced D&A Americas.

• GenAI initiatives for Advanced D&A Americas.

MAJOR / KEY ACCOUNTABILITIES

• Build, maintain, and optimize end to end ML pipelines covering data ingestion, feature engineering, training, evaluation, deployment, inference and monitoring using Databricks and related tooling.

• Collaborate closely with Data Scientists to translate experimental and research grade models into reliable, scalable, and secure production services that meet business and technical requirements.

• Apply MLOps best practices including model versioning, experiment tracking, monitoring, and automated deployments.

• Develop and deploy traditional ML models (e.g., regression, classification, forecasting, NLP) to solve business problems.

• Implement runtime monitoring dashboards and alerting mechanisms to detect performance degradation, data anomalies, and system failures in near real time.

• Support AI / GenAI initiatives, including LLM based prototypes and production workflows where applicable.

• Collaborate with product owners, data scientists, engineers, and business stakeholders to define model requirements, SLAs, success metrics, and deployment constraints.

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