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Added 1 min ago
JI
Linehaul Payroll/Admin Clerk
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- Full-time
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M
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Senior Engineer, Geotechnical
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TC
Vice President, Client Integration
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Added 16 mins ago
UO
Assistant to the Executive Director, Student Support
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- $60,068.35 to $84,095.69 CAD / year
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…and curiosity shape our world for the better. Our teaching, learning and work environment is uniquely strengthened and enriched by Indigenous perspectives. With two main campuses in…
Added 16 mins ago
NB
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Added 16 mins ago
EV
Senior Engineer, Geotechnical
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Added 16 mins ago
B
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Added 16 mins ago
Group Underwriter, New Business
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MR
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Added 16 mins ago
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Added 16 mins ago
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Added 16 mins ago
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EXFO

Scientifique des données sénior

Quebec, QC, Canada

Full-time

Apply About employer

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Job details

Job type: Full-time
Apply by: Nov 25, 2026
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.

Role Summary

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.

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

Analyze product, operational, customer, and network data; test hypotheses; build models; and define reliable metrics to guide decisions. Establish analytical standards, mentor colleagues, support Applied AI evaluation, and collaborate across teams to turn insights into actions.

Requirements

Requires a university degree in a quantitative field and 5 or more years of experience in data analysis, applied statistics, and modeling; a master's degree is an asset. Candidates should be proficient in Python, R, SQL, statistical and machine learning libraries, and Git, with strong communication and influential leadership skills, and be bilingual in French and English.

Skills

Statistical Analysis
Data Exploration
Descriptive Modeling
Predictive Modeling
Hypothesis Testing
Metric Definition
Data Quality Assessment
Python
R
SQL
Machine Learning
Git
Data Visualization and Communication
Mentoring
Data Governance
Bilingual Communication

Visa sponsorship

Not detected in the job text

Keywords

Data Science
Statistical Analysis
Data Exploration
Descriptive Modeling
Diagnostic Modeling
Predictive Modeling
Hypothesis Testing
Metrics
Key Performance Indicators
Data Readiness
Data Quality
Data Governance
Python
R
SQL
Statistical Libraries
Machine Learning
Git
Ground Truth
Evaluation Datasets
Applied AI
MLOps
Data Preparation
Telecommunications
Network Data
Product Analytics
Operational Analytics
Mentoring
Analytical Standards
Bilingual Communication

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