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Added 11 mins ago
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…developing or influencing automated frameworks for enterprise-level functional and regression…
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Quality Engineering Specialist
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Added 54 mins ago
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Added 54 mins ago
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Senior Data Scientist / Toronto, ON (Hybrid) - FTE
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Added 54 mins ago
System Engineering - Platforms
General Dynamics Mission Systems–Canada
Ottawa, ON
Full-time
Coordinate with cross-functional teams to establish and maintain necessary lab resources – including specialized equipment, testing infrastructure and workspace - specific to integration…
Added 54 mins ago
Quality Engineering Specialist
General Dynamics Mission Systems–Canada
Ottawa, ON
$77,000 to $98,000 CAD / year
Full-time
Proven ability to conduct internal audits, lead investigations, and lead cross-functional teams to resolution.

AceStack

Senior Data Scientist / Toronto, ON (Hybrid) - FTE

Toronto, ON, Canada

Job type: Full-time
Apply by: Oct 28, 2026
Posting language: English
Posted: Sep 28, 2026

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Original job description

Senior Data Scientist

Location: Toronto, ON

Work Model: Hybrid

Experience: 10+

Job Summary

We are seeking an experienced Senior Data Scientist to support the AI Center of Excellence (AI CoE) across machine learning, data science, and advanced analytics initiatives. The ideal candidate will have strong expertise in applied machine learning, statistical modeling, predictive analytics, and data-driven problem solving, with the ability to translate complex business challenges into practical, scalable data science solutions.

This is a highly analytical and hands-on role responsible for delivering end-to-end data science solutions, from data exploration and feature engineering through model development, validation, interpretation, and communication of actionable insights.

Key Responsibilities

Build, evaluate, and optimize machine learning models, including regression, classification, forecasting, and predictive models.
Perform data exploration, preparation, cleansing, transformation, and feature engineering to develop high-quality modeling datasets.
Apply statistical and machine learning techniques to solve complex business and analytical problems.
Develop and validate models using appropriate model evaluation, experimentation, and statistical methodologies.
Design and analyze A/B tests and experiments to measure business and product impact.
Generate actionable insights from structured and unstructured data and communicate findings to technical and business stakeholders.
Translate business requirements and analytical challenges into practical data science and machine learning solutions.
Partner with Data Scientists, ML Engineers, Data Engineers, and business teams to move analytical solutions toward production readiness.
Interpret model results, identify trends and patterns, and communicate recommendations through clear visualizations and presentations.
Contribute to enterprise AI and analytics initiatives from problem definition and exploration through model development, validation, and production readiness.

Required Skills & Experience

Strong hands-on experience in Python and SQL.
Strong knowledge of machine learning, statistical modeling, predictive analytics, and data science methodologies.
Experience with:
Regression and classification
Forecasting and predictive modeling
Feature engineering
Model evaluation and validation
Experimentation and A/B testing
Data analysis and insights generation
Strong analytical and problem-solving skills with the ability to work with large and complex datasets.
Ability to independently manage data science initiatives while collaborating effectively with cross-functional teams.
Strong communication skills with the ability to explain complex analytical and model outputs to both technical and non-technical stakeholders.

Preferred / Nice-to-Have Skills

Experience with Azure, Azure Machine Learning (Azure ML), Databricks, MLflow, or similar cloud-based data science platforms.
Experience with data visualization tools such as Tableau, Power BI, or similar platforms.
Experience developing solutions for use cases such as:
Churn prediction
Customer/member personalization
Recommendation systems
Marketing optimization
Anomaly detection
Experimentation frameworks
Experience working with ML model lifecycle management, MLOps, and productionization.
Experience working in enterprise-scale AI/ML or analytics environments.

Ideal Candidate

The ideal candidate combines strong technical depth in Python, SQL, machine learning, and statistical modeling with strong business problem-solving and communication skills. They should be comfortable working independently, collaborating across data and engineering teams, and delivering data science solutions that can progress from exploration to production.

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More about this job

Responsibilities

Develop, evaluate, and optimize machine learning and statistical models, including preparing data, engineering features, and validating results. Translate business challenges into scalable data science solutions, communicate actionable insights, and collaborate with data, engineering, and business teams to advance solutions toward production.

Requirements

Requires 10+ years of experience, strong hands-on Python and SQL skills, and expertise in machine learning, statistical modeling, predictive analytics, experimentation, and working with complex datasets. Candidates should be able to manage initiatives independently, collaborate across teams, and clearly communicate analytical findings to technical and non-technical stakeholders.

Skills

Python
SQL
Machine Learning
Statistical Modeling
Predictive Analytics
Regression
Classification
Forecasting
Feature Engineering
Model Evaluation
Model Validation
A/B Testing
Data Analysis
Data Visualization
Azure Machine Learning
Databricks

Visa sponsorship

Not detected in the job text

Keywords

Python
SQL
Machine Learning
Statistical Modeling
Predictive Analytics
Regression
Classification
Forecasting
Feature Engineering
Model Evaluation
Model Validation
A/B Testing
Experimentation
Data Analysis
Structured Data
Unstructured Data
Azure
Azure Machine Learning
Databricks
MLflow
Tableau
Power BI
MLOps
Model Lifecycle Management
Churn Prediction
Personalization
Recommendation Systems
Marketing Optimization
Anomaly Detection
AI Center of Excellence

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