Senior Data Analytical Specialist​/Scientist

We are seeking an advanced Senior Data Analytical Specialist/Scientist to analyze, build, and validate complex, large-scale statistical models within enterprise production environments. Working with minimal direction, you will bridge the gap between advanced research methodologies and scalable data engineering. You will collaborate closely with cross-functional technical teams, data stewards, and data engineers to design reproducible data workflows, execute model validation, and transition specialized quantitative frameworks into sustainable production systems.

Location:

Toronto, ON (Hybrid – 2 days per week in office)

Duration: 6-month contract

Public Sector

Experience:

Required.

Advantages
Advanced Modeling Focus:
Translate complex statistical models and research logic into scalable production workflows.

Modern Analytics Stack:
Leverage R, Python, version-controlled workflows, and cloud analytics platforms.

Hybrid Work Model:
Enjoy a flexible balance with 2 days per week in the Toronto office.

High-Impact

Collaboration:

Partner directly with data engineers, architects, and technical leads across the full delivery lifecycle.

Responsibilities
Analyze complex statistical methodologies and model logic to translate research and analytical specifications into scalable, production-ready builds.

Build, validate, and execute complex statistical models within enterprise analytics environments and cloud platforms.

Design and implement end-to-end data ingestion, transformation, quality checking, and reproducible data processing workflows using R or Python.

Collaborate with data engineers, architects, data stewards, and business leads to confirm requirements, data acquisition pathways, and delivery decisions.

Validate model assumptions, perform data quality assessments, and troubleshoot statistical or code-level issues.

Document analytical methods, code repositories, and operational workflows to conduct formal knowledge transfer and ensure long-term platform sustainability.

Prepare clear technical documentation, briefing materials, and presentations to explain complex quantitative methods, outputs, and limitations to non-technical stakeholders.

Qualifications
Statistical Modeling & Validation Seniority:
Senior-level experience developing and validating statistical methods and models, including reproducible analytical workflows and underlying data requirements (Strictly Required).

Data Processing & Pipeline Workflow Design:
Hands-on experience designing data-ingestion, transformation, quality-checking, and reproducible data-processing workflows (Strictly Required).

Technical Scripting &

Languages:

High proficiency in R and/or Python, query languages (SQL), and version-controlled analytical repositories (Strictly Required).

Stakeholder & Consulting Excellence:
Strong consulting and relationship-management skills to collaborate with clients, technical teams, and data stewards (Strictly Required).

Quantitative Communication:
Proven ability to communicate complex quantitative methods, assumptions, outputs, and limitations clearly to both technical and non-technical audiences (Strictly Required).

Public Sector Expertise:
Prior experience working within public sector IT or data analytics environments (Strictly Required).

Desirable Assets:

Experience with Power BI visualization tools and cloud analytics platforms (e.g., Azure, Databricks).

Background in population health data, chronic disease modeling, or adapting academic/research models into production systems.

Familiarity with digital accessibility standards (such as AODA guidelines).

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,…