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Added 1 min ago
A
Executive Assistant, CIO
AIMCo
Calgary, AB
- Full-time
…competing priorities while enabling the CIO office and the senior investment leadership team to operate at their best. This Role is located in our Calgary office in a hybrid…
Added 1 min ago
I
Directeur adjoint, Architecture de solutions d’IA/ Associate Principal, AI Solution Architecture
IQVIA
Ottawa, ON
- Full-time
The Associate Principal, AI Solution Architecture is a senior client-facing leader within Defensible Data & AI (DDAI) responsible for shaping, architecting, and delivering trusted AI…
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Senior Analyst, Fraud Strategy
Momentum Financial Services Group
Toronto, ON
As a Senior Analyst, Fraud Strategy you will be accountable for the daily fraud prevention activities at Momentum that ensure applicable fraud rules, processes and reporting are in place to…
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Senior Underwriter - Commercial Lines Mid Market-Maritimes
Wawanesa Insurance
Moncton, NB · Dartmouth, NS
Working under minimal supervision, the Senior Underwriter – Commercial Lines, Mid Market is responsible for analyzing the acceptability of complex and high premium volume business according…
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Senior Manager, Planning
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- Full-time
On behalf of our client, Ganong Bros Ltd, we are looking for an experienced Senior Manager, Planning to join their team, supporting the continued growth and evolution of their supply chain…
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- Full-time
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Forward Deployed Engineer (Python/Pyspark Developer)
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Toronto, ON
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DM
Senior Zoho Developer
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Toronto, ON
- Full-time
We're looking for a Senior Zoho Developer to help us accelerate the development, customization, and optimization of our internal business systems.
Added 15 mins ago
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Human Resources Business Partner
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- Full-time
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Added 15 mins ago
1
Marketing Technology Lead Developer
1-800-GOT-JUNK?
Vancouver, BC · Toronto, ON
- Full-time
Background: Profile of a Staff Engineer or Senior Full-Stack Web Developer with 5-8+ years of web development experience and a proven track record of supporting marketing initiatives in a…
Added 15 mins ago
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Senior Software Engineer, Backend - Fraud Prevention
Sardine
Remote, Canada
- $180,000 to $206,000 CAD / year
- Full-time
- Remote
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Added 31 mins ago
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Added 31 mins ago
Member IT Services - Technical Lead- BC Functional
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Added 31 mins ago
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Engineer, Cloud Operations
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- Full-time
…providing opportunities to learn and grow under the guidance of senior team members in a fast-paced, evolving…
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Senior Project Manager
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Winnipeg, MB
- $80 to $100 CAD / hour
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Community Outreach and Marketing Manager
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Toronto, ON · Greater Toronto Area, Canada
- Full-time
Take ownership of community outreach within a growing organization, with direct access to senior leadership and room to bring your own ideas.
Added 31 mins ago
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Linux System Engineer
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Job Title: Senior DevOps Engineer with Linux and HPC
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Added 31 mins ago
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Assistant General Counsel - Insurance Regulatory & Corporate
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Toronto, ON · Greater Toronto Area, Canada
- $180,000 to $220,000 USD / year
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Added 31 mins ago
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Added 31 mins ago
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Added 46 mins ago
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Maintenance Manager
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Added 46 mins ago
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EXFO

Scientifique des données sénior

Montreal, QC, Canada
Quebec, QC, Canada

Full-time

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Job details

Job type: Full-time
Experience: Expérience significative (5+ ans) en analyse de données, statistique appliquée et modélisation descriptive/prédictive dans un contexte produit ou opérationnel.
Posting language: English · French
Posted: Oct 26, 2026

Original job description

English section of this bilingual listing.

The Senior Data Scientist extracts value from data through statistical analysis, data exploration, descriptive and predictive modeling, and the definition of reliable metrics to support product, operational, and AI-related decision-making. Beyond analytical execution, this role serves as a technical reference and influential leader within the data practice, elevating analytical standards, mentoring junior analysts, establishing modeling best practices, and helping shape analytical priorities in close partnership with the Data Lead.

Key Responsibilities

Explore, analyze, and interpret product, operational, customer, and network data to identify insights that support business decisions.
Formulate hypotheses, test them using rigorous statistical methods, and distinguish between correlation, probable causation, bias, and interpretation limitations.
Develop analyses, segmentations, and descriptive, diagnostic, or predictive models to explain phenomena, anticipate trends, and prioritize actions.
Define, validate, and document metrics, KPIs, thresholds, cohorts, and calculation methodologies required to measure value and performance.
Assess data readiness, including coverage, quality, freshness, granularity, representativeness, bias, data gaps, and usage limitations.
Establish and continuously improve analytical standards across the data practice, including methodologies, modeling best practices, quality criteria, reusability, and analytical rigor.
Demonstrate influential leadership within the data organization by coaching peers, sharing knowledge, challenging analytical assumptions, and contributing to decisions regarding priorities, risks, and investment efforts.
Support the Applied AI team in defining ground truth datasets, evaluation datasets, performance metrics, and exploratory analyses required for AI models.
Collaborate with Data Ops, Data Governance Specialists, UX, AI Transformation, Product Managers, and Software Developers to frame problems, prioritize analytical initiatives, and convert insights into actionable outcomes.

Qualifications

University degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field (Master's degree is an asset).
Significant experience (5+ years) in data analysis, applied statistics, and descriptive/predictive modeling in a product or operational environment.
Proficiency in analytical languages and tools (Python, R, SQL), common statistical and machine learning libraries, and version control systems such as Git.
Ability to communicate complex analytical findings in a clear and actionable manner to non-technical audiences.
Demonstrated ability to provide influence-based leadership through mentoring, facilitating best practices, aligning contributors, and recommending analytical priorities in a multidisciplinary environment.
Bilingualism in French and English, both spoken and written, as the role requires communication with teams outside Canada.

Assets

Experience working closely with Applied AI and/or MLOps teams on data preparation and preprocessing activities.
Understanding of data governance and data quality principles, including collaboration with Data Governance Specialists.
Experience in the telecommunications, networking, or a similar industrial sector.

EXFO is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Read the full bilingual original

Job Description:

Sommaire du rôle

Le Scientifique des données sénior extrait de la valeur des données par l'analyse statistique, l'exploration, la modélisation descriptive/prédictive et la définition de métriques fiables, afin d'éclairer les décisions produit, opérationnelles et AI. Au-delà de l'exécution analytique, il agit comme référence technique et influenceur de leadership au sein du pôle données : il élève la qualité des pratiques analytiques, encadre et fait grandir les analystes plus juniors, structure les standards de modélisation et contribue à orienter les priorités analytiques en étroite collaboration avec le Data Lead.

Responsabilités principales

Explorer, analyser et interpréter les données produit, opérationnelles, clients ou réseau afin d'identifier les signaux utiles à la décision.
Formuler des hypothèses, les tester avec des méthodes statistiques rigoureuses et distinguer corrélation, causalité probable, biais et limites d'interprétation
Construire des analyses, segmentations, modèles descriptifs, diagnostiques ou prédictifs pour expliquer des phénomènes, anticiper des tendances ou prioriser des actions.
Définir, valider et documenter les métriques, indicateurs, seuils, cohortes et règles de calcul nécessaires au pilotage de la valeur et de la performance.
Qualifier la data readiness : couverture, qualité, fraîcheur, granularité, représentativité, biais, trous de données et limites d'usage.
Établir et faire évoluer les standards analytiques du pôle : méthodes, bonnes pratiques de modélisation, critères de qualité des analyses, réutilisabilité des approches et rigueur d'interprétation.
Exercer un leadership d'influence dans le pôle données : coacher les pairs, partager les apprentissages, challenger les hypothèses analytiques et contribuer aux arbitrages sur les priorités, risques et efforts à investir.
Supporter Applied AI dans la définition de ground truth, jeux d'évaluation, métriques de performance et analyses exploratoires nécessaires aux modèles.
Collaborer avec les Data Ops, Spécialistes en gouvernance de données, UX, IA Transformation, Gestionnaires de produits et Développeurs Logiciels pour cadrer les problèmes, prioriser les initiatives analytiques et transformer les insights en actions.

Profil recherché

Formation universitaire en statistique, mathématiques, informatique, science des données ou domaine quantitatif connexe (maîtrise un atout).
Expérience significative (5+ ans) en analyse de données, statistique appliquée et modélisation descriptive/prédictive dans un contexte produit ou opérationnel.
Maîtrise des langages et outils d'analyse (Python, R, SQL), des bibliothèques de modélisation statistique/ML courantes et des logiciels de versionnement comme Git
Capacité à vulgariser des résultats analytiques complexes en recommandations claires et actionnables pour des audiences non techniques.
Capacité démontrée à exercer un leadership d'influence : mentorat, animation de pratiques, alignement de contributeurs et recommandation de priorités analytiques dans un environnement multidisciplinaire.·
Bilinguisme français-anglais, à l'oral et à l'écrit (la personne aura à communiquer avec des équipes à l'extérieur du Canada).

Atouts

Expérience de collaboration étroite avec des équipes Applied AI ou MLOps sur la préparation et la prétraitement des données.
Connaissance des enjeux de gouvernance et de qualité des données (interaction avec les Spécialistes en gouvernance de données).
Expérience en secteur télécommunications, réseaux ou environnement industriel similaire.

Role Summary

Key Responsibilities

Explore, analyze, and interpret product, operational, customer, and network data to identify insights that support business decisions.
Formulate hypotheses, test them using rigorous statistical methods, and distinguish between correlation, probable causation, bias, and interpretation limitations.
Develop analyses, segmentations, and descriptive, diagnostic, or predictive models to explain phenomena, anticipate trends, and prioritize actions.
Define, validate, and document metrics, KPIs, thresholds, cohorts, and calculation methodologies required to measure value and performance.
Assess data readiness, including coverage, quality, freshness, granularity, representativeness, bias, data gaps, and usage limitations.
Establish and continuously improve analytical standards across the data practice, including methodologies, modeling best practices, quality criteria, reusability, and analytical rigor.
Demonstrate influential leadership within the data organization by coaching peers, sharing knowledge, challenging analytical assumptions, and contributing to decisions regarding priorities, risks, and investment efforts.
Support the Applied AI team in defining ground truth datasets, evaluation datasets, performance metrics, and exploratory analyses required for AI models.
Collaborate with Data Ops, Data Governance Specialists, UX, AI Transformation, Product Managers, and Software Developers to frame problems, prioritize analytical initiatives, and convert insights into actionable outcomes.

Qualifications

University degree in Statistics, Mathematics, Computer Science, Data Science, or a related quantitative field (Master's degree is an asset).
Significant experience (5+ years) in data analysis, applied statistics, and descriptive/predictive modeling in a product or operational environment.
Proficiency in analytical languages and tools (Python, R, SQL), common statistical and machine learning libraries, and version control systems such as Git.
Ability to communicate complex analytical findings in a clear and actionable manner to non-technical audiences.
Demonstrated ability to provide influence-based leadership through mentoring, facilitating best practices, aligning contributors, and recommending analytical priorities in a multidisciplinary environment.
Bilingualism in French and English, both spoken and written, as the role requires communication with teams outside Canada.

Assets

Experience working closely with Applied AI and/or MLOps teams on data preparation and preprocessing activities.
Understanding of data governance and data quality principles, including collaboration with Data Governance Specialists.
Experience in the telecommunications, networking, or a similar industrial sector.

EXFO is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Apply About employer

More about this job

Responsibilities

The Senior Data Scientist extracts value from data through statistical analysis, modeling, and metric definition to support product and operational decision-making. They also act as a technical leader by mentoring junior analysts, establishing modeling standards, and collaborating with cross-functional teams to drive analytical priorities.

Requirements

Candidates must have a university degree in a quantitative field and at least 5 years of experience in data analysis and statistical modeling. Proficiency in Python, R, and SQL is required, along with strong leadership skills and bilingual proficiency in French and English.

Skills

Statistical Analysis
Predictive Modeling
Descriptive Modeling
Python
R
SQL
Machine Learning
Data Visualization
Data Governance
Mentoring
Leadership
Communication
Git
Applied Statistics
Data Readiness

Visa sponsorship

Not detected in the job text

Keywords

Data Science
Statistical Analysis
Predictive Modeling
Descriptive Modeling
Python
R
SQL
Machine Learning
Git
Data Governance
Data Quality
Applied AI
MLOps
Telecommunications
KPIs
Data Readiness
Mentorship
Cross-functional Collaboration
Product Analytics
Operational Analytics
Segmentation
Data Interpretation
Version Control
Statistical Methods
Business Intelligence

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