senior data analytical specialist/scientist.

We are seeking a highly accomplished Senior Data Analytical Specialist/Scientist for an enterprise-level contract opportunity based in Toronto. In this role, you will take on a premier data science and machine learning capacity, specializing in building statistical models, executing batch inferences, and deploying Large Language Model (LLM) frameworks.

As a principal data scientist, you will bridge the gap between complex unstructured datasets, cloud machine learning infrastructure, and actionable business insights. Operating fully onsite in Toronto, you will lead the execution of analytics initiatives, optimize pipeline evaluations, prepare text datasets, and build data models that enable evidence-based decision-making. This position is ideal for an analytics authority who can manage experiment tracking in Databricks/MLflow, communicate complex quantitative analysis clearly, and guide functional teams through advanced AI/ML implementations.

Location: Toronto, ON

Assignment Type: Onsite (5 days per week)

Contract Duration: 4-month contract (with potential for extension)

Advantages

Cutting-Edge Generative AI Stack: Direct hands-on development using modern LLM frameworks (DSPy, LangChain, Hugging Face).

Cloud ML & Databricks Ecosystem: Manage batch inferences, experiment tracking, and model monitoring in Azure Databricks and MLflow environments.

End-to-End Analytics Ownership: Own data transformation, pipeline evaluation, machine learning model building, and output annotation.

Stable Onsite Engagement: Secure a full-time contract embedded within a collaborative, data-driven environment in Toronto.

Responsibilities
Lead the end-to-end development and delivery of advanced functional analytics, statistical models, and machine learning algorithms to support decision-making.

Execute, monitor, and optimize batch inference jobs within Databricks and cloud machine learning environments, troubleshooting issues and logging results.

Support pipeline executions using DSPy, loading custom models/configurations, and running evaluation runs using internal performance metrics.

Prepare, clean, structure, and transform complex, high-volume structured and unstructured text datasets for model inference and evaluation.

Annotate model outputs to conduct error analysis, track performance metrics, and establish benchmarks for inference experiments.

Maintain reproducible scripts and notebooks to ensure consistent execution of model training, experimentation, and inference runs.

Partner with functional area experts, Data Architects, and ETL Developers to translate complex business challenges into scalable Business Intelligence solutions.

Design high-quality interfaces, interactive dashboards, and visual summaries to present quantitative analysis to technical and non-technical stakeholders.

Conduct end-user training, document system components, and provide expert guidance on transforming raw analytics into proactive, actionable insights.

Qualifications
Core Technical & AI/ML Requirements
Education & Background: Academic degree or background in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, or a quantitative discipline.

Cloud Data & ML Tools: Hands-on experience with Azure data tools, specifically Azure, Databricks, and MLflow for experiment tracking and model management.

LLM Frameworks: Direct exposure to Large Language Model (LLM) frameworks such as DSPy, LangChain, Hugging Face, or equivalent architectures.

Programming & Data Libraries: Strong proficiency in Python and core data science libraries, including pandas, scikit-learn, PyTorch, or PySpark.

Analytical & Data Manipulation Skills: Proven ability to process, transform, and analyze complex, high-volume data from diverse relational and multi-dimensional sources.

Preferred Assets
Analytical Software: Familiarity with additional analytical tools such as R, Matlab, SPSS, SAS, PowerPivot, or advanced Excel/VBA.

Data Management: Broad understanding of database architecture, ETL pipelines, and information visualization methodologies.

Soft Skills & Collaboration
Communication & Consulting: Excellent oral, written, and presentation skills with a demonstrated ability to explain complex quantitative concepts to non-technical stakeholders.

Organization & Detail: Detail-oriented with strong organizational skills for tracking experiments, managing log outputs, and troubleshooting model anomalies.

Summary
If you're interested in the "Senior Data Analytical Specialist/Scientist" role based in Toronto, we encourage you to apply online at
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 View email address on randstad.com to ensure their ability to fully participate in the interview process.

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