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Data Reviewer Jobs in Quebec (NOW HIRING)

Review major analytical and modeling deliverables for clarity, rigor, quality, consistency, and ... Improve data science delivery by identifying opportunities for better workflows, stronger ...

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Data Platform Engineer

Montreal, QC ยท On-site

CA$65 - CA$67/hr

In this role, you will design and improve scalable data solutions, help guide technical decisions, review and write code, support team members, and promote strong engineering practices. Position ...

New

This is a hybrid strategy-and-architecture role reporting to the Director, Data; you will move comfortably from a CTO whiteboard conversation to an EDI 837 parser to a model-evaluation review without ...

Applications are reviewed as they come in-don't wait, apply today and join our team. * Follow detailed instructions and understand UPC attributes for the purpose of data collection. * Maintain in ...

Data Developer II

Sherbrooke, QC ยท On-site

CA$35.06 - CA$46/hr

As a Data Developer II, you will be responsible for designing, implementing, and maintaining GEM ... can review and consider how we may be able to assist you based on your individual needs. Global ...

Data Developer II

Sherbrooke, QC ยท On-site

CA$35.06 - CA$46/hr

As a Data Developer II, you will be responsible for designing, implementing, and maintaining GEM ... we can review and consider how we may be able to assist you based on your individual needs. We ...

Our Talent Acquisition team will review your profile to ensure it aligns with our requirements and ... Define a build data tracking methods and processes * Work with engineers to create data tracking ...

Regularly review and enhance system and data architectures to ensure alignment with evolving business needs and technology advancements. * Solution Design & Delivery: Collaborate with cross ...

Regularly review and enhance system and data architectures to ensure alignment with evolving business needs and technology advancements. Solution Design & Delivery: Collaborate with cross-functional ...

Review transformations and provide sign-off for each migration cycle. * Data loading and validation. Coordinate and take part in test loads into sandbox and production environments. Lead validation ...

New

About the Role We are seeking a Staff Data Developer to join our Data & Intelligence division ... If your application is declined automatically, you may request a human review. We're committed to ...

... reviewing and screening applications. These tools support, but do not replace, human judgment in ... Strong data analysis skills with experience applying statistical methods such as exploratory data ...

New

Support the Project Management Office (PMO) in its data management tasks, including data ... These tools do not make hiring decisions; all applications are reviewed by a human recruiter, and ...

Showing results 21-40

Data Reviewer information

See Quebec salary details

$13

$23

$39

How much do data reviewer jobs pay per hour?

As of Aug 13, 2026, the average hourly pay for data reviewer in Quebec is $23.73, according to ZipRecruiter salary data. Most workers in this role earn between $15.38 and $34.86 per hour, depending on experience, location, and employer.

What is the difference between Data Reviewer vs Data Analyst?

AspectData ReviewerData Analyst
Required CredentialsTypically a bachelor's degree in data management, IT, or related fields; certifications like CDMP are commonBachelor's degree in statistics, data science, or related fields; certifications like CAP or Microsoft certifications are common
Work EnvironmentMostly office-based, working with data validation tools and softwareOffice or remote, analyzing data sets, creating reports, and visualizations
Employer & Industry UsageUsed in industries like finance, healthcare, and government for data quality assuranceUsed across industries for data-driven decision making and reporting

While both roles involve working with data, Data Reviewers focus on validating and ensuring data accuracy, whereas Data Analysts interpret data to generate insights. Understanding these differences helps in choosing the right career path or job search focus.

What is a data reviewer?

A data reviewer helps an organization review and interpret data for accuracy and interpretation. Data reviewers are necessary for many fields, including software development, quality assurance, medical and health care professions, and accounting, to name a few. Your responsibilities and duties are to look through collected data that has been entered into a spreadsheet or other database. You check it for any errors and manage issues you find. Some data reviewer positions, such as in medical research, include an analytical component; you help the research team to glean insight from the collected data.

What are the key skills and qualifications needed to thrive as a data reviewer, and why are they important?

To thrive as a Data Reviewer, you need strong analytical skills, attention to detail, and typically a background in life sciences, statistics, or a related field. Familiarity with data management systems, electronic data capture (EDC) platforms, and compliance standards such as GCP is commonly required. Excellent problem-solving, critical thinking, and communication skills help you identify discrepancies and collaborate with cross-functional teams. These competencies are crucial for ensuring data integrity, regulatory compliance, and the reliability of research outcomes.

How does a data reviewer typically collaborate with other teams to ensure data quality?

Data Reviewers work closely with data entry specialists, analysts, and project managers to verify the accuracy and consistency of datasets. They often participate in cross-functional meetings to discuss data discrepancies and establish best practices for data validation. This collaboration helps maintain high-quality data standards and ensures that any issues are promptly identified and resolved, supporting the overall goals of the organization.
What are popular job titles related to Data Reviewer jobs in Quebec? For Data Reviewer jobs in Quebec, the most frequently searched job titles are:
What job categories do people searching Data Reviewer jobs in Quebec look for? The top searched job categories for Data Reviewer jobs in Quebec are:
What are popular job titles related to Data Reviewer jobs in QC? For Data Reviewer jobs in QC, the most frequently searched job titles are:
Infographic showing various Data Reviewer job openings in Quebec as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, and 3% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $49,350 per year, or $23.7 per hour.

Senior Data Scientist

Valtech

Montreal, QC โ€ข On-site

Full-time

Medical, Retirement

Posted 6 days ago


Job description

Why Valtech? We're the experience innovation company - a trusted partner to the world's most recognized brands. To our people we offer growth opportunities, a values-driven culture, international careers and the chance to shape the future of experience.ย 

The opportunity

At Valtech, you'll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking or building the next generation of customer experiences, your work will help transform industries.ย 

We are proud of:ย 

  • The work we do and the innovation we driveย 
  • Our values of share, care and dareย 
  • A workplace culture that fosters creativity, diversity and autonomyย 
  • Our borderless, global framework, which enables seamless collaborationย 
The roleย ย 

Please note, we are only accepting applicants from the provinces of Ontario and Quebec for this role.ย For Quebec-based candidates, fluency in English is necessary because the position entails collaboration with teams based in the rest of Americas and occasionally in Europe.

The Senior AI Data Scientist is a senior individual contributor role within Data Science, AI, & Agentic. This role is responsible for leading complex analytical, predictive, and applied AI workstreams, operating with a high degree of independence and serving as a trusted advisor on data science and AI strategy.
This role focuses on shaping and executing advanced modeling, experimentation, and applied AI solutions that address high-value business problems. The Senior AI Data Scientist translates ambiguous stakeholder needs into structured analytical approaches, model designs, evaluation frameworks, and actionable insights. They partner closely with cross-functional teams and stakeholders to ensure that analytical and AI-driven solutions are both technically sound and practically impactful.
At this level, the role combines strong quantitative expertise with deep modeling capability, rigorous experimentation practices, and thoughtful application of AI methods. The Senior AI Data Scientist influences analytical direction, improves delivery quality through best practices and reusable approaches, and elevates the overall effectiveness of data science workstreams across engagements.

Role responsibilities

  • Lead complex analytical, statistical, machine learning, and applied AI workstreams across multiple business areas, use cases, or stakeholder groups.
  • Define data science approaches that align business questions, modeling opportunities, evaluation methods, and measurable outcomes.
  • Translate ambiguous business and stakeholder needs into structured analytical strategies, model designs, hypotheses, feature approaches, validation plans, and actionable recommendations.
  • Lead the design and execution of models and analyses across use cases such as segmentation, forecasting, propensity modeling, anomaly detection, experimentation analysis, recommendation-oriented analysis, and business decision support.
  • Guide the use of structured, semi-structured, and selected unstructured datasets to derive insights and build business-relevant solutions.
  • Own and improve notebook-based development, reproducible workflows, and analytical assets in Databricks and other cloud-based environments.
  • Apply machine learning and AI methods to support classification, scoring, summarization, pattern detection, feature generation, and business process improvement use cases.
  • Evaluate and apply LLM-enabled or AI-assisted workflows where they strengthen analysis, insight generation, decision support, or analytical productivity, while preserving statistical rigor, reproducibility, and human accountability.
  • Establish and reinforce best practices for methodology selection, model evaluation, experimentation design, documentation quality, and reproducibility.
  • Synthesize modeling outputs, analytical findings, and applied AI results into clear business implications and recommended next steps.
  • Serve as a senior partner to client and internal stakeholders by advising on analytical tradeoffs, model usefulness, evaluation rigor, and solution direction.
  • Review major analytical and modeling deliverables for clarity, rigor, quality, consistency, and business usefulness, and help raise standards across engagements through reusable patterns and stronger delivery practices.
  • Collaborate with AI Scientists, AI Engineers, Analytics Engineers, Data Engineers, and Architects to align solutions with business needs, data realities, platform constraints, and technical patterns.
  • Mentor junior and mid-level practitioners through technical guidance, quality review, and best-practice sharing, without formal people-management responsibility.
  • Improve data science delivery by identifying opportunities for better workflows, stronger evaluation, clearer documentation, more scalable notebooks, and reusable analytical assets.
  • Follow established governance, privacy, and responsible data and AI use standards in day-to-day work.
Core Skills/Competencies
  • Deep working knowledge of statistics, probability, machine learning, experimentation, and analytical problem solving.ย 
  • Strong ability to define data science approaches and modeling strategies in complex business environments.ย 
  • Strongย peopleย leadership skills, including coaching, feedback, prioritization, and support for team development.ย 
  • Strong understanding of supervised and unsupervised learning, feature engineering, model evaluation, experimentation design, and error analysis.ย 
  • Strong ability to work with structured, semi-structured, and selected unstructured datasets.ย 
  • Strong familiarity with applied AI methods, including LLM-enabled workflows, text-oriented analysis, AI-assisted feature extraction, summarization, and classification.ย 
  • Strong familiarity with notebook-based development and collaborative data science workflows, including Databricks andย MLflow-supported experimentation.ย 
  • Ability to evaluate where applied AI strengthens a use case and where classical statistical or machine learning methods are moreย appropriate.ย 
  • Strong stakeholder management skills and the ability to communicate clearly with technical and non-technical audiences.ย 
  • Ability to balance delivery quality, team workload, business urgency, and stakeholder expectations across multiple workstreams.ย 
  • Strong written and verbal communication skills in English, including confidence in client-facing and leadership-facing settings.ย 
  • Ability to collaborate effectively across distributed teams in the Americas and across multiple disciplines.ย 
AI Fluency/AI-Assisted Data Science Expectations
  • Expected to be an active adopter of approved AI-enabled analytical, coding, experimentation, documentation, and productivity workflows that improve the quality and speed of data science work. Uses AI-assisted workflows to support exploratory analysis, feature thinking, code and notebook development, model documentation, experiment design, analytical summarization, and stakeholder communication while maintaining human accountability for method selection, statistical reasoning, validation, interpretation, and final recommendations.
  • Understands that AI-generated code, modeling suggestions, analytical summaries, or methodological recommendations must be reviewed against source data, assumptions, statistical rigor, business context, governance expectations, and reproducibility standards before use. Demonstrates curiosity and practical enthusiasm for applying AI to improve analytical leverage, decision support, and delivery quality without weakening scientific discipline or human judgment.
  • At this level, AI fluency means using AI responsibly while helping a team adopt AI-enabled practices with consistency and care. Expected to coach practitioners on safe, useful, and role-appropriate AI adoption; review AI-assisted outputs for quality and governance; and improve team delivery habits without weakening accountability or craft standards.ย 
Tools / Platformsย 

Programming / Data Scienceย 

  • Pythonย 
  • Jupyterย Notebooksย 
  • Pandasย 
  • NumPyย 
  • scikit-learnย 
  • SciPyย 
  • Statsmodelsย 
  • XGBoostย 
  • LightGBMย 

Data Science Workbench / Lakehouse Platformsย 

  • Databricksย 
  • Databricks notebooksย 
  • Databricks Machine Learningย 
  • Apache Sparkย 
  • PySparkย 
  • MLflowย 

Data & Queryingย 

  • SQLย 
  • BigQueryย 
  • Snowflakeย 
  • Other cloud data platforms as neededย 

Cloud & AI Platformsย 

  • Google Cloud Platform (GCP)ย 
  • Vertex AIย 
  • Microsoft Azureย 
  • Azure AI servicesย 
  • Azure Machine Learningย 
  • Other cloud-based machine learning and analytics platforms as neededย 

Applied AI / LLM Supportย 

  • OpenAI-compatible APIs or enterprise LLM platforms as relevant to the client environmentย 
  • Prompt evaluation and structured testing workflowsย 
  • Embedding, text analysis, and unstructured data processing patternsย 
  • Model and workflow evaluation tooling as relevant to the client environmentย 

Visualization / Analysis Supportย 

  • Matplotlibย 
  • Seabornย 
  • Plotlyย 
  • Lookerย 
  • Power BIย 
  • Tableauย 

Workflow / Collaboration / Versioningย 

  • Gitย 
  • GitHubย 
  • Azure DevOpsย 
  • Other collaboration and code management tools as relevant to the client environmentย 

Certifications Preferred, not requiredย 

  • Databricks associate or professional-level training or certificationย 
  • Google Cloud data, ML, or AI trainingย 
  • Microsoft Azure data, ML, or AI trainingย 
  • Python, machine learning, experimentation, or applied AI courseworkย 
  • Statistics, forecasting, or analytical modeling trainingย 
  • Leadership, coaching, or people management training is a plusย 
Commitment to reaching all kinds of peopleย 

We design experiences that work for all kinds of people - and that starts with our own teams. At Valtech, we're intentional about building an inclusive culture where everyone feels supported to grow, thrive and achieve their goals. No matter your background, you belong here. Explore our Diversity & Inclusion siteย to see how we're creating a more equitable Valtech for all.ย 

ย The benefits ย 

This is a full time position based in Canada. The offered salary range is $70,000 - 120,000ย  CADย annually, depending on experience and location.ย 

Valtech offers a comprehensive benefits package effective after three months of continuous service:

  • A comprehensive insurance plan, where you can choose the module that best suits your needs-Gold, Silver, or Bronze. The employer may contribute up to 80% of your coverage depending on the selected module. This plan includes short- and long-term disability coverage.
  • Dialogue via Sun Life provides virtual healthcare services, allowing you to consult with a healthcare professional for emergencies, prescription renewals, and more. You also have access to the Employee and Family Assistance Program, as well as a complete mental health support program.
  • A $500 Personal Spending Account, which can be used for healthcare reimbursements, gym memberships, public transit passes, office supplies, or contributions to your RRSP through Valtech.
  • A retirement plan where Valtech will match 100% of your RRSP contributions through a Deferred Profit Sharing Plan (DPSP), up to a maximum of 4%. You can start contributing to your RRSP immediately, and to the DPSP after 3 months. The vesting of the DPSP will be after a 24 months of service.ย 
  • Access to a flexible vacation under Valtech's policy to support your work-life balance, with 5 days available during your probation period and a prorated amount calculated for the remainder of the year.
  • Personal Technology Reimbursement - $30/month for every employee-offered on day 1.ย 
  • We close during the winter holidays and offer flexible scheduling throughout the year, so you can enjoy those sunny Friday afternoons-provided your weekly hours are completed.
Your application process

Once you apply, our Talent Acquisition team will review your application. If your skills and experience align with the role, we'll reach out for next steps. Your CV should cover key information on relevant experiences and expertise. We do not require information such as age, gender, marital status, or a headshot in your application. We review all candidates based on skills, experience, and potential.

Beware of recruitment fraud: Only engage with official Valtech email addresses.

We are committed to inclusion and accessibility. If you need reasonable accommodations during the interview process, please either indicate it in your application or let your Talent Partner know.ย 

About Valtech

Valtech is the experience innovation company that exists to unlock a better way to experience the world. By blending crafts, categories, and cultures, we help brands unlock new value in an increasingly digital world.ย 

At the intersection of data, AI, creativity, and technology, we drive transformation for leading organizations, including L'Oreal, Mars, Audi, P&G, Volkswagen Dolby, and more.ย 

At Valtech, we don't just talk about transformation. We make it happen. Our people are the heart of our success, and we foster a workplace where everyone has the support to thrive, grow and innovate.ย 

Are you ready...