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Open nowFirst seen 4 hours ago

RQ00242 - Data Science Developer - Senior

Flexstaf IT95 open roles

Where
Toronto, ON, Canada
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Your applicationOpen nowRQ00242 - Data Science Developer - SeniorFlexstaf IT · Toronto, ON, Canada

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The clock on this job

Early applications get read.

13.3% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.1%1 day
  2. 4.6%3 days
  3. 13.3%7 days
  4. 21.3%14 days
  5. 35.7%30 days
  6. 59.1%60 days
This job: first seen 4 hours ago

The posting

Supplier Comments

MSP Notes

Shortlist Date: Nov 9, 2026 10:00am

Must Haves:

  • Deep expertise in statistics, probability, predictive modelling and machine learning, including classification, regression, clustering, feature engineering, experimentation, model selection, validation, bias/variance, overfitting and statistical inference.
  • Advanced hands-on proficiency in Python, SQL and ML frameworks, with demonstrated experience building, tuning and validating models using real-world datasets. Tech stack: Python, SQL, Power BI, VSCode, Git, Jira, Azure (OpenAI, Foundry and ML Studio), Databricks.
  • Strong hands-on experience with Azure Machine Learning and Microsoft Foundry/Azure AI Foundry, including end-to-end ML pipelines covering experimentation, training, evaluation and operationalization.

Location: Hybrid – 3 days a week onsite

Public Sector Experience: preferred but not required

# of submissions/supplier: 1

Job Title

Senior Data Analytical Specialist/Scientist

Description

The Senior Data Analytical Specialist/Scientist will develop advanced analytics, predictive models, and AI-driven insights to address priority business opportunities. The role will apply statistical, machine learning, and analytical techniques to enterprise data, while supporting data maturity assessments, model validation, business-value analysis, and the identification of high-value AI and analytics opportunities.

Key Responsibilities

  • Design and develop advanced analytics, predictive models, and machine learning solutions for priority business use cases.
  • Conduct exploratory data analysis to identify patterns, trends, relationships, and opportunities for improved decision-making.
  • Translate business problems into appropriate analytical and modelling approaches, defining hypotheses, methodologies, and success measures.
  • Assess the quality, availability, and readiness of data required for AI and advanced analytics use cases.
  • Support enterprise data maturity and analytics capability assessments and identify opportunities for improvement.
  • Validate model performance using appropriate statistical and machine learning techniques and communicate model assumptions, limitations, and results.
  • Partner with business stakeholders to identify high-value AI and analytics opportunities and assess potential business value and feasibility.
  • Develop prototypes and proofs of concept to test analytical approaches before broader implementation.
  • Work closely with Data Engineers, Data Architects, Product Managers, and technology teams to operationalize successful models and analytical solutions.
  • Define and monitor analytical KPIs and support evaluation of whether delivered solutions achieve intended business outcomes.
  • Communicate complex analytical findings and recommendations in clear, actionable terms for both technical and executive audiences.
  • Apply appropriate Responsible AI, privacy, data governance, and model risk considerations throughout the analytics lifecycle.

Qualifications & Experience

  • Degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, or a related quantitative field, or equivalent experience.
  • Significant experience in data science, advanced analytics, predictive modelling, machine learning, or statistical analysis.
  • Strong proficiency in Python, SQL, statistical analysis, and machine learning techniques.
  • Experience developing and validating predictive and machine learning models using real-world enterprise data.
  • Knowledge of model evaluation, experimentation, feature engineering, data quality, and analytical methodology.
  • Experience working with modern cloud-based data and analytics environments.
  • Understanding of Generative AI and emerging AI techniques would be an asset.
  • Strong business acumen with the ability to connect analytical findings to measurable business outcomes.
  • Strong communication, problem-solving, and stakeholder-management skills.
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