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Manager Data Scientist Jobs in Quebec (NOW HIRING)

About the role We are looking for a manager to join the Intact Lab - Data Science team in the AI in Operations family! This role particularly aims at supporting the development and deployment of AI ...

About the role Intact is looking for a Data Scientist to turn complex data into practical solutions ... Time Management: Strong organizational and time management skills to handle multiple projects and ...

About the role Intact is looking for a Data Scientist to turn complex data into practical solutions ... Time Management: Strong organizational and time management skills to handle multiple projects and ...

About the role Intact is looking for a Data Scientist to turn complex data into practical solutions ... Time Management: Strong organizational and time management skills to handle multiple projects and ...

As a credit risk data scientist, you help meet the need for assessing credit risk in the granting ... Balance business needs, risk management, requirements and best practices * Lead or contribute to ...

Data and Applied Scientist The context engine that makes AI enterprise ready. Anyone can build an ... Excellent communication and stakeholder management skills, with the ability to work cross ...

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Manager Data Scientist information

See Quebec salary details

$25K

$118.9K

$195.5K

How much do manager data scientist jobs pay per year?

As of Aug 30, 2026, the average yearly pay for manager data scientist in Quebec is $118,856.00, according to ZipRecruiter salary data. Most workers in this role earn between $84,500.00 and $155,500.00 per year, depending on experience, location, and employer.

What is a manager data scientist?

Manager Data Scientists are professionals who oversee data science teams and projects within an organization. They combine advanced analytical skills with leadership abilities to guide data scientists, set project priorities, and ensure data-driven strategies align with business goals. In addition to technical expertise in data modeling, machine learning, and analytics, they are responsible for mentoring team members, managing resources, and communicating insights to stakeholders. Their role bridges the gap between technical execution and strategic decision-making.

What are the key skills and qualifications needed to thrive as a manager data scientist?

To thrive as a Manager Data Scientist, you need expertise in statistical analysis, machine learning, data modeling, and a relevant degree such as in computer science, mathematics, or statistics. Familiarity with tools like Python, R, SQL, cloud platforms (e.g., AWS, Azure), and experience with data visualization software and project management methodologies are commonly required. Strong leadership, effective communication, and the ability to mentor and guide teams are vital soft skills in this role. These competencies ensure successful project delivery, drive data-driven business decisions, and foster a productive, innovative team environment.

How does a manager data scientist typically collaborate with cross-functional teams to drive business outcomes?

As a Manager Data Scientist, you will work closely with teams such as engineering, product management, and business stakeholders to ensure data-driven solutions align with company goals. This collaboration often involves translating complex analytical findings into actionable insights, setting project priorities, and managing expectations. You will also facilitate communication between data scientists and non-technical teams to foster understanding and ensure successful project delivery. Building strong relationships and promoting a culture of data-driven decision-making are essential aspects of the role.

What is the difference between Manager Data Scientist vs Data Scientist?

AspectManager Data ScientistData Scientist
Required CredentialsBachelor's or Master's in Data Science, Statistics, or related field; leadership experienceBachelor's or Master's in Data Science, Statistics, or related field
Work EnvironmentLeads teams, manages projects, collaborates with stakeholdersAnalyzes data, develops models, reports findings
Employer & Industry UsageUsed in organizations with data teams, tech, finance, healthcareFound across industries, entry to mid-level roles

The main difference is that a Manager Data Scientist oversees data teams and projects, focusing on leadership and strategic planning, while a Data Scientist primarily conducts data analysis and model development. The manager role involves more coordination, mentorship, and stakeholder communication, whereas the data scientist role emphasizes technical skills and hands-on analysis.

What are the most commonly searched types of Data Scientist jobs in Quebec?

The most popular types of Data Scientist jobs in Quebec are:

What cities in Quebec are hiring for Manager Data Scientist jobs?

Cities in Quebec with the most Manager Data Scientist job openings:

Infographic showing various Manager Data Scientist job openings in Quebec as of August 2026, with employment types broken down into 87% Full Time, 12% Part Time, and 1% Contract. Highlights an 79% Physical, 2% Hybrid, and 19% Remote job distribution, with an average salary of $118,856 per year, or $57.1 per hour.

Junior Data Scientist

Expretio Technologies

Montreal, QC โ€ข On-site

Full-time

Re-posted 12 days ago


Job description

Join our team and take your career to the next level.

Job Summary:

Expretio recherche une Data Scientist junior pour rejoindre son equipe R&D dynamique basee a Montreal. Au sein d'une equipe AI, vous contribuerez au developpement et au support d'Appia, notre solution phare de Revenue Management pour l'industrie ferroviaire, en soutenant l'analyse de donnees, la modelisation predictive et l'analyse du comportement d'achat des clients.
En tant que Data Scientist junior, vous evoluerez dans un role a forte composante operationnelle et analytique : preparation et validation des donnees, investigations sur la qualite des donnees, tableaux de bord et production de resultats clairs pour l'equipe. En parallele, vous serez amene a developper des preuves de concept en Python visant a contribuer a l'amelioration de composants de la chaine d'analyse et de monitoring.
Ce poste constitue un point d'entree ideal dans l'ecosysteme d'Intelligence Artificielle et de Revenue Management d'Expretio. Vous collaborerez etroitement avec des Data Scientists, des developpeurseuses IA et des scientifiques en recherche operationnelle experimentes, tout en developpant vos competences techniques et votre connaissance du domaine. Il s'agit d'un role pense pour evoluer : a mesure que vous progresserez, la science des donnees et la modelisation prendront une plus grande part de vos responsabilites.
------------------------------------ ENGLISH -----------------------
Expretio is looking for a Junior Data Scientist to join its dynamic R&D team based in Montreal. As part of an AI team, you will contribute to the development and support of Appia, our flagship Revenue Management solution for the rail industry, supporting data analysis, predictive modeling, and customer purchasing behavior analysis.
As a Junior Data Scientist, you will work in a role with a strong operational and analytical focus: data preparation and validation, data quality investigations, dashboards, and the production of clear results for the team. In parallel, you will develop proof-of-concept solutions in Python aimed at contributing to the improvement of components within our analysis and monitoring chain.
This role is an ideal entry point into Expretio's Artificial Intelligence and Revenue Management ecosystem. You will work closely with experienced Data Scientists, AI developers, and operations research scientists, while developing your technical skills and domain knowledge. This role is designed to grow: as you progress, data science and modeling will take on a greater share of your responsibilities.

Job Description:

Votre role

Analyse de donnees et soutien operationnel

Soutenir les investigations sur la qualite des donnees et les analyses statistiques

Contribuer a la conception et la mise a jour de tableaux de bord et a la visualisation de donnees

Produire des syntheses claires et des rapports de resultats analytiques

Utiliser les outils d'IA generative pour appuyer l'analyse, l'extraction et les taches d'outillage interne

Participer a l'evolution et l'automatisation de la chaine de calibration et de monitoring

Verifier et documenter la qualite et la coherence des resultats produits

Science des donnees et modelisation

Developper en Python des preuves de concept pour des composants d'analyse et de modelisation predictive

Contribuer a l'amelioration des composants et algorithmes mettant en jeu de la science des donnees

Creer des tests de validation de modeles et la documentation des fonctionnalites analytiques

Participer a l'amelioration de l'architecture de science des donnees et des modeles predictifs dans Appia

Collaboration et developpement

Collaborer avec les clients pour comprendre les comportements et adapter les modeles a leurs besoins

Collaborer avec l'equipe de Support pour analyser et resoudre les problemes de Production

Participer aux retrospectives et proposer des ameliorations continues

Profil recherche

Formation et experience

Baccalaureat en science des donnees, mathematiques, statistiques ou domaine connexe (maitrise un atout)

Experience de stage ou de projet academique en apprentissage automatique et en analyse de donnees

Toute formation complementaire pertinente en science des donnees est consideree comme un atout

La maitrise de l'anglais, tant a l'ecrit qu'a l'oral, est requise pour permettre une communication efficace dans un environnement organisationnel et commercial international. L'entreprise opere a l'echelle mondiale, avec des clients a travers le monde, faisant de l'anglais la langue de travail commune entre les regions.

Competences techniques

Competences en analyse exploratoire de donnees et en controle de la qualite des donnees

Capacite a interpreter les donnees et a en tirer des observations exploitables

Bonne connaissance de Python pour la manipulation et l'analyse de donnees (pandas)

Comprehension des modeles de Machine Learning, notamment les methodes ensemblistes (Random Forest, bagging) et les algorithmes de boosting (XGBoost, LightGBM, gradient boosting)

Notions de modelisation predictive et d'analyse de series temporelles

Bonnes bases en SQL et en manipulation de donnees

Maitrise d'outils de visualisation de donnees (Power BI equivalent), incluant la conception de rapports interactifs

Capacite a communiquer clairement des resultats analytiques en interne comme aux clients

Utilisation des outils d'IA generative pour des taches d'analyse

Capacite a communiquer efficacement en francais et en anglais

Atout : Experience avec Google OR-Tools dans un contexte d'optimisation

Qualites personnelles

Curiosite et forte volonte d'apprendre de nouvelles technologies et concepts

Souci du detail dans l'analyse de donnees et la validation de modeles

Capacite a communiquer avec des parties prenantes variees, en adaptant son discours a chaque audience

Aptitude au travail d'equipe et a recevoir la retroaction de maniere constructive

Sens de la rigueur et de la gestion du temps

Conscience des enjeux d'IA responsable lors de l'utilisation d'outils d'IA generative

Environnement technique

Methodologies : Agile (Scrum), Lean (Kanban)

Science des donnees : Python, pandas, PyTorch, LightGBM, MLflow

Bases de donnees : SQL (PostgreSQL), NoSQL (MongoDB)

Infrastructure : Docker/Podman, GitLab, Jenkins

Outils : Claude, Pycharm, Office 365, Jira, Confluence, PowerBI, Miro
---------------- ENGLISH -----------------------

Key Responsibilities

Data Analysis and Operational Support

Support data quality investigations and statistical analyses

Contribute to the design and updating of dashboards and data visualizations

Produce clear summaries and reports of analytical results

Use generative AI tools to support analysis, extraction, and internal tooling tasks

Participate in the evolution and automation of the calibration and monitoring chain

Verify and document the quality and consistency of the results produced

Data Science and Modeling

Develop proof-of-concept solutions in Python for analysis and predictive modeling components

Contribute to the improvement of components and algorithms involving data science

Create model validation tests and documentation for analytical features

Contribute to the improvement of the data science architecture and predictive models within Appia

Collaboration and Development

Work with clients to understand behaviors and adapt models to their needs

Work with the Support team to analyze and resolve Production issues

Take part in retrospectives and propose continuous improvements

What you bring to the role

Preferred Education and Experience

Bachelor's degree in data science, mathematics, statistics, or a related field (Master's an asset)

Internship or academic project experience in machine learning and data analysis

Any relevant additional training in data science is considered an asset

Technical Skills

Skills in exploratory data analysis and data quality control

Ability to interpret data and derive actionable insights

Solid knowledge of Python for data manipulation and analysis (pandas)

Understanding of Machine Learning models, particularly ensemble methods (Random Forest, bagging) and boosting algorithms (XGBoost, LightGBM, gradient boosting)

Familiarity with predictive modeling and time series analysis

Solid foundations in SQL and data manipulation

Proficiency with data visualization tools (Power BI or equivalent), including the design of interactive reports

Ability to clearly communicate analytical results, both internally and to clients

Use of generative AI tools for analysis tasks

Ability to communicate effectively in French and English

Asset: Experience with Google OR-Tools in an optimization context

Other Key Skills and Competencies

Curiosity and a strong willingness to learn new technologies and concepts

Attention to detail in data analysis and model validation

Ability to communicate with a variety of stakeholders, tailoring message to each audience

Ability to work in a team and receive feedback constructively

Rigor and good time management

Awareness of responsible AI considerations when using generative AI tools

Technical Environment

Methodologies: Agile (Scrum), Lean (Kanban)

Data Science: Python, pandas, PyTorch, LightGBM, MLflow

Databases: SQL (PostgreSQL), NoSQL (MongoDB)

Infrastructure: Docker/Podman, GitLab, Jenkins

Tools: Claude, Pycharm, Office 365, Jira, Confluence, PowerBI, Miro

Worker Type:

Regular

Number of Openings:

1