Original job description
We are seeking an experienced Senior Data Science Developer to lead the development of advanced analytics, predictive models, and machine learning solutions that address critical enterprise business opportunities. In this role, you will apply rigorous statistical and machine learning methodologies to complex datasets, formulate analytical hypotheses, conduct in-depth exploratory data analysis, and build high-impact models. Working across the Azure Data & AI ecosystem and Databricks, you will design end-to-end machine learning pipelines from experimentation and feature engineering through validation and operationalization, partnering closely with data engineers, architects, and cross-functional teams to deliver reliable, business-driven insights.
Location: Toronto, ON (Hybrid – 3 days a week onsite)
Duration: 6-month contract.
Advantages
Modern AI Tech Stack: Hands-on exposure to Azure AI Foundry, Azure OpenAI, Azure ML Studio, and Databricks.
Full-Lifecycle Impact: Lead initiatives from hypothesis generation and PoC prototyping through production operationalization.
Business-Critical Focus: Directly influence strategic decision-making through advanced predictive modeling.
Hybrid Flexibility: Balanced hybrid work arrangement based in Toronto (3 days onsite per week).
Responsibilities
Design, build, train, and validate advanced predictive models, statistical frameworks, and machine learning solutions for key enterprise use cases.
Translate complex business problems into structured analytical approaches, establishing clear hypotheses, methodologies, and quantitative success metrics.
Conduct exploratory data analysis (EDA) to uncover trends, relationships, anomalies, and actionable insights across enterprise data sources.
Build, tune, and evaluate machine learning models (classification, regression, clustering) while diagnosing bias, variance, and overfitting.
Develop automated, end-to-end ML pipelines for data preparation, feature engineering, model training, evaluation, and operationalization.
Build rapid prototypes and proofs of concept (PoCs) to test analytical approaches prior to enterprise deployment.
Partner with Data Engineers, Data Architects, and Product Managers to transition validated models into production environments.
Define and track model performance metrics and analytical KPIs to ensure delivered solutions achieve measurable business outcomes.
Present complex analytical findings, model assumptions, and strategic recommendations in clear terms for executive and technical audiences.
Embed Responsible AI, privacy, data governance, and model risk mitigation practices throughout the analytics lifecycle.
Qualifications
Statistical & Machine Learning Mastery: Deep expertise in statistics, probability, predictive modeling, and machine learning, including classification, regression, clustering, feature engineering, experimentation, model selection, validation, bias/variance trade-offs, and statistical inference (Strictly Required).
Hands-on Python, SQL & Frameworks: Advanced hands-on proficiency in Python, SQL, and core machine learning frameworks, with proven experience building, tuning, and validating models using real-world enterprise datasets (Strictly Required).
Technical Stack Proficiency: Practical experience across the primary toolset: Python, SQL, Power BI, VS Code, Git, Jira, Azure (OpenAI, Foundry, ML Studio), and Databricks (Strictly Required).
Azure Machine Learning & AI Foundry: Strong hands-on experience utilizing Azure Machine Learning and Microsoft Foundry / Azure AI Foundry to build end-to-end ML pipelines covering experimentation, training, evaluation, and deployment (Strictly Required).
Analytical Translation & Communication: Demonstrated ability to translate business requirements into analytical solutions and articulate technical concepts to both technical and executive stakeholders (Strictly Required).
Education & Background: Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field, or equivalent practical experience (Strictly Required).
Nice-to-Have Assets:
Prior data science, analytics, or machine learning experience within the public sector.
Familiarity with Generative AI techniques, Large Language Models (LLMs), and prompt engineering.
Industry certifications in Azure AI or Data Science (e.g., Azure Data Scientist Associate).
Summary
If you're interested in the "Senior Data Science Developer" role based in Toronto, we encourage you to apply online at www.randstad.ca.
Only qualified candidates will be contacted for the next steps. We look forward to hearing from you!
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 accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
This posting is for existing and upcoming vacancies.
