Research Machine Learning Scientist

toronto dominion bank; Toronto; Hybrid; CAD 120,000 - 160,000; Full time

Job summary

Randstad Canada in Toronto is seeking a Senior Data Scientist for an enterprise-level contract based in Toronto. You will bridge business requirements with cloud data architectures, building AI/ML models, ETL pipelines, and interactive dashboards in a hybrid setting.

You will leverage Azure Databricks, Synapse, AML, and Azure OpenAI services to deploy predictive analytics, NLP, and data-driven decisions. A minimum of 5 years in data science and strong Python/SQL skills are required.

Qualifications

• 5+ years of progressive experience in data science, predictive modeling, machine learning, and advanced analytics.
• 5+ years of hands-on experience with Azure SQL Server, Azure Synapse Analytics, and Azure Databricks.
• 5+ years of hands-on experience writing Python and SQL for data manipulation and analytics.
• Proven experience deploying AI/ML models using AML and Azure OpenAI services.
• Expertise with Apache Spark and large-scale data processing.
• Experience creating executive dashboards and Power BI reports.

Responsibilities

• Collaborate with cross-functional teams to identify operational needs and develop data-driven solutions.
• Design, build, deploy, and optimize predictive ML models using AML and OpenAI.
• Construct and maintain ETL pipelines using Azure Databricks, Synapse, and Spark.
• Perform feature engineering, modeling, and data mining to derive insights.
• Develop interactive reports and dashboards with Power BI for executives.
• Enforce data governance, monitor model performance, and support cloud architecture.
• Document designs, write technical specs, and prepare status reports.

Skills

Python

SQL

Azure Databricks

Azure Synapse

Power BI

ML/DL

Azure Machine Learning

Spark

Tools

Azure SQL Server

Azure Databricks

Azure Synapse Analytics

Azure OpenAI

Job description

We are seeking a highly accomplished Senior Data Scientist for an enterprise-level contract opportunity based in Toronto. In this role, you will take on a premier engineering capacity within artific...

As a principal data scientist, you will bridge the gap between complex business requirements, modern cloud data architectures, and predictive machine learning models. Operating within a hybrid work model, you will lead the design and deployment of AI/ML models, build scalable ETL pipelines, process large-scale datasets using cloud platforms, and create intuitive business intelligence dashboards. This position demands an analytics expert who can leverage machine learning algorithms, natural language processing (NLP), and deep learning techniques to drive intelligent automation and data-informed decision‑making.

Location: Toronto, ON

Assignment Type: Hybrid (Minimum 2 days per week onsite)

Contract Duration: 52 weeks (12 months)

Advantages

High-Impact AI/ML Scope: Architect and deploy advanced predictive models, NLP algorithms, and intelligent automation solutions.

Modern Azure Cloud Ecosystem: Deepen technical expertise using Azure Databricks, Azure Synapse, Azure Machine Learning (AML), and Azure OpenAI services.

Large-Scale Data Analytics: Process and transform high-volume structured and unstructured datasets using Apache Spark and Python.

Flexible Hybrid Environment: Work within a collaborative hybrid arrangement combining onsite teamwork in Toronto with remote flexibility.

Responsibilities

Collaborate with cross-functional teams to identify key operational needs, conduct exploratory data analysis, and implement scalable, data-driven solutions.

Design, build, deploy, and optimize predictive machine learning, deep learning, and AI models using Azure Machine Learning (AML) and Azure OpenAI.

Construct and maintain robust ETL pipelines, processing and transforming large-scale datasets using Azure Databricks, Azure Synapse, and Apache Spark.

Perform feature engineering, statistical modeling, data mining, and predictive analytics to extract actionable business insights.

Design and publish interactive reports, executive data visualizations, and dashboards using Power BI and modern analytics tooling.

Enforce best practices in data governance, model performance monitoring, scalable cloud architecture, and data virtualization.

Support all phases of testing (Systems Integration Testing, QA, UAT) by validating model accuracy, tuning performance, and resolving technical defects.

Author comprehensive technical design specifications, functional documentation, presentation decks, and status reports to support operational transition.

Qualifications

Core Technical & Data Science Requirements

Data Science & ML Tenure: 5+ years of progressive experience in data science, predictive modeling, machine learning, and advanced analytics.

Azure Cloud Data Stack: 5+ years of hands‑on experience utilizing Azure SQL Server, Azure Synapse Analytics, and Azure Databricks.

Programming Mastery: 5+ years of hands‑on experience writing complex data manipulation and analytics code in Python and SQL.

AI/ML Model Deployment: Proven experience building and deploying AI/ML solutions using Azure Machine Learning (AML) and Azure OpenAI services.

Big Data Processing: Expertise with Apache Spark, data lake architectures, dimensional modeling, and processing large‑scale structured and unstructured data.

Data Visualization: Hands‑on expertise creating executive dashboards, semantic models, and reporting views using Power BI or similar tools.

Preferred Assets & Certifications

Desirable Certifications: Active designations such as Microsoft Certified: Azure Data Engineer Associate or Microsoft Certified: Azure AI Engineer Associate.

Accessibility Knowledge: Familiarity with digital accessibility standards and human rights frameworks (such as OHRC and AODA).

Soft Skills & Professional Attributes

Analytical Problem Solving: Proven ability to analyze complex business challenges, derive actionable insights from multi‑source data, and communicate technical solutions clearly.

Adaptability & Collaboration: Strong interpersonal, communication, and collaboration capabilities with a fast‑learning mindset to adopt emerging AI/ML technologies.

Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity‑seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non‑binary/gender non‑conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.

Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to [e-mail] to ensure their ability to fully participate in the interview process.