IT & data jobs in Canada

Explore it & data jobs in Canada. Choose a role or place to find work that fits.

Counted Sep 28, 2026

Hiring at a glance

View jobs

Open jobs: 2,656
Employers: 1,315

Advertised starting pay

Average starting payCAD / year: $98,294Across 257 listings
Average starting payCAD / hour: $50.16Across 86 listings
Average starting payUSD / year: $117,285Across 53 listings

Employer-advertised starting pay, not employee salaries. Currencies and pay periods stay separate.

Where work happens

Remote: 5%130 listings
Hybrid: 39%1,033 listings
On-site: 56%1,478 listings

Not specified: 1% (15 listings)

Employers hiring include RBC (48), Royal Bank of Canada (43), TD (41).

Sources & calculation notes

These figures describe the listings on this role page at the date shown, not the whole job market. Listings can change.

Employer-listed minimums from at least 10 listings per currency and pay period. These are advertised starting amounts, not employee salaries. Jobs without a minimum and unusual amounts are excluded.

Hybrid and on-site classifications may be inferred from descriptions. Check the original ad. How we count

IT & data jobs in Canada

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Charger Logistics Inc.

Sr. Data Scientist

Brampton, ON, Canada

Job type: Full-time
Apply by: Oct 25, 2026
Experience: 6+ years of hands-on experience in data science and machine learning/AI, delivering production-grade ML solutions.
Posting language: English
Posted: Sep 25, 2026

Apply About employer

Applications open in a new tab.

Original job description

Charger Logistics Inc. is a leading asset-based transportation company with over 20 years of experience delivering innovative logistics solutions. We have evolved into a world-class transport provider and continue to expand across North America.

We invest in our people, fostering an environment where learning, growth, and career advancement are encouraged. As an entrepreneurial organization, we value initiative, creativity, and forward-thinking strategies.
We are looking for a Sr. Data Scientist to develop, deploy, and scale machine learning (ML) and AI solutions for fleet analytics, logistics optimization, and operational decision-making. This is a hands-on role focusing on production-grade ML, real-time and streaming analytics, and AI-driven decision systems built on cloud platforms, including Google Cloud, Kafka, and RisingWave.

Responsibilities:

Design, develop, and deploy production-grade ML models for fleet optimization, including route optimization, ETA prediction, fuel efficiency, capacity planning, predictive maintenance, and driver behavior analysis.
Build anomaly detection, forecasting, and time-series models to monitor vehicle health, trip deviations, fuel theft, and demand fluctuations.
Develop batch and real-time ML pipelines with low-latency inference using Kafka, RisingWave, and cloud services.
Integrate large language models (OpenAI, Google MCP, Ollama, Hugging Face) for conversational analytics, automated insights, and retrieval-augmented generation (RAG) systems.
Operate MLOps workflows on Google Cloud using Vertex AI Pipelines, Feature Store, and Model Registry, supporting model training, deployment, monitoring, and drift detection.
Build and optimize end-to-end data pipelines for analytics and ML using BigQuery, Dataflow, Dataproc, Vertex AI, Cloud Functions, Pub/Sub, and Cloud Composer (Airflow).
Design scalable analytical data models in BigQuery, AlloyDB PostgreSQL, and Snowflake; optimize SQL-based feature engineering, data partitioning, and clustering.
Perform exploratory data analysis (EDA) to uncover trends, anomalies, and business insights.
Build dashboards and visualizations for stakeholders.
Collaborate with cross-functional teams to translate business problems into robust data science solutions.
Support best practices in model development, experimentation, documentation, and data governance.

Requirements

Bachelor's degree or equivalent in Data Analytics, Statistics, Mathematics, or Computer Science.
6+ years of hands-on experience in data science and machine learning/AI, delivering production-grade ML solutions.
Strong experience in Python, including libraries such as Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow, XGBoost, and LightGBM.
Advanced SQL skills, including CTEs, window functions, and query optimization.
Hands-on experience with Google Cloud, including Vertex AI (training, pipelines, deployment, feature store) and BigQuery (data modeling, performance tuning).
Experience with streaming platforms (Kafka, RisingWave) and Snowflake.
Knowledge of anomaly detection, time-series forecasting, optimization, and applied statistical modeling.
Experience deploying and monitoring ML models in production, including testing, and working with ETL/orchestration tools like Matillion, Airflow, and Cloud Composer.
Familiarity with advanced ML and AI techniques, including LLMs, geospatial or graph ML, computer vision, and GPS data analysis.
Solid understanding of knowledge retrieval patterns including RAG (Retrieval-Augmented Generation), with familiarity of emerging approaches like KAG (Knowledge-Augmented Generation) and CAG (Cache-Augmented Generation).
Experience with Azure, AWS, GCP, Databricks, or multi-cloud deployments is a plus.
Excellent communication and problem-solving skills, with the ability to thrive in fast-paced environments.
Certifications: Google Cloud Professional Data Engineer or Machine Learning Engineer is an asset; SnowPro® Advanced: Data Scientist certification preferred.

Benefits

Competitive Salary
Healthcare Benefit Package
Career Growth

Apply About employer

More about this job

Responsibilities

Develop, deploy, and scale production machine learning and AI solutions for fleet optimization, logistics analytics, forecasting, anomaly detection, and operational decision-making. Build batch and real-time data and ML pipelines, manage cloud-based MLOps workflows, create analytical models and dashboards, and collaborate with teams to turn business needs into data science solutions.

Requirements

Requires a bachelor's degree or equivalent in a relevant quantitative or computing field and at least six years of hands-on data science and machine learning experience delivering production solutions. Candidates should have strong Python and SQL skills, experience with Google Cloud, Vertex AI, BigQuery, streaming platforms, and production ML operations, plus knowledge of forecasting, optimization, LLMs, and retrieval methods.

Skills

Machine Learning
Python
Advanced SQL
Google Cloud
Vertex AI
BigQuery
Kafka
RisingWave
Snowflake
Time-Series Forecasting
Anomaly Detection
MLOps
Large Language Models
Retrieval-Augmented Generation
Data Pipelines
Data Visualization

Benefits

Competitive Salary
Healthcare Benefit Package
Career Growth

Visa sponsorship

Not detected in the job text

Keywords

Python
Pandas
NumPy
Scikit-Learn
PyTorch
TensorFlow
XGBoost
LightGBM
Google Cloud
Vertex AI
BigQuery
Kafka
RisingWave
Snowflake
AlloyDB PostgreSQL
Dataflow
Dataproc
Cloud Functions
Pub/Sub
Cloud Composer
Airflow
Matillion
MLOps
Machine Learning
Large Language Models
Retrieval-Augmented Generation
Time-Series Forecasting
Anomaly Detection
Geospatial Machine Learning
Google Cloud Professional Data Engineer

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