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Remote Data Science Tutor Jobs in Quebec (NOW HIRING)

It is a 100% Remote position in Canada. \n * Candidates Preferred from EST\/CST Time Zone. \n JOB ... data science\/ML. \n * Create repeatable reference implementations for common digital product ...

Effective collaboration with multidisciplinary teams of geologists, geophysicists, geochemists, and data scientists. * Process, analyze, and interpret geophysical, geological, geochemical, remote ...

Having recognized the advantages of remote work, including employee morale, productivity, reduced ... You will identify, coordinate, and develop Data Science solutions to build predictive/prescriptive ...

Manager AI

Montreal, QC · On-site +1

Bachelor's or Master's degree in Engineering, Data Science, Computer Science, or related fields Work Environment * Location: Anywhere in Canada (hybrid or fully remote work arrangement)

Manager AI

Quebec, QC · On-site +1

Bachelor's or Master's degree in Engineering, Data Science, Computer Science, or related fields Work Environment * Location: Anywhere in Canada (hybrid or fully remote work arrangement)

New

... data science, AI, engineering innovation, and IoT. Our customers include the world's leading public ... With almost every team remote by default, Canonical sets the pace on the 21st-century digital ...

This is a fully remote role. About the Role You will lead the team that turns TailorCare's data ... Primary Responsibilities Lead a team of outcome-driven data scientists and ML engineers, with ...

... 100% Remote - Excellent working conditions *** Reporting to the VP of Technology, you will collaborate with teams across software engineering, data science, regulatory, security, and external ...

Enhance operational workflows through better tooling, automation, and data * Turn commercial ... Remote or in-office work * Personalized benefits program * $1,500 for professional training and ...

You'll have the opportunity to work on an amazing platform that combines Data Analytics, and ... A B.Sc. in Computer Science/Computer Engineering Some AWESOME selling points: * Remote + flexible ...

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Remote Data Science Tutor information

What are the key skills and qualifications needed to thrive as a Remote Data Science Tutor, and why are they important?

To thrive as a Remote Data Science Tutor, you need a solid background in data science concepts, programming (Python or R), and statistical analysis, usually backed by a degree in a related field and tutoring or teaching experience. Familiarity with tools like Jupyter Notebook, Zoom, and learning management systems, as well as relevant certifications (e.g., Coursera or edX credentials), is typically required. Excellent communication, patience, and the ability to explain complex topics in simple terms help tutors connect with learners and address diverse questions. These skills are essential for effectively guiding remote students, fostering engagement, and ensuring successful learning outcomes in a virtual environment.

What are the most common challenges faced by remote data science tutors, and how can they be addressed?

Remote data science tutors often face challenges such as engaging students virtually, managing diverse learning paces, and ensuring clear communication of complex concepts. To address these, it's helpful to leverage interactive tools like virtual whiteboards and coding platforms, tailor lesson plans to individual student needs, and maintain regular check-ins. Building a supportive online community and providing timely feedback further enhance the learning experience for students and make tutoring more effective.

What are Remote Data Science Tutors?

Remote Data Science Tutors are professionals who provide instruction and guidance in data science topics through online platforms rather than in-person sessions. They help students or professionals understand concepts such as statistics, machine learning, data analysis, and programming languages like Python or R. By working remotely, these tutors can offer flexible scheduling and reach students in different geographic locations. Their goal is to support learners in building practical skills and solving real-world data science problems.

What is the difference between Remote Data Science Tutor vs Data Science Instructor?

AspectRemote Data Science TutorData Science Instructor
CredentialsTypically requires a degree in data science, statistics, or related fields; certifications like CAP or DASCA are commonOften requires advanced degrees and teaching certifications; industry experience is valued
Work EnvironmentWorks remotely, often one-on-one or small groups via online platformsCan be remote or in-person, usually in educational institutions or training centers
Employer & Industry UsageFreelance, online tutoring platforms, educational startupsUniversities, colleges, corporate training programs

While both roles involve teaching data science concepts, a Remote Data Science Tutor typically provides personalized, online tutoring to individual students or small groups, focusing on specific skills or projects. A Data Science Instructor usually teaches larger classes in academic or corporate settings, covering broader curricula. The key difference lies in the scope, environment, and audience of each role.

What job categories do people searching Remote Data Science Tutor jobs in Quebec look for? The top searched job categories for Remote Data Science Tutor jobs in Quebec are:
What cities in Quebec are hiring for Remote Data Science Tutor jobs? Cities in Quebec with the most Remote Data Science Tutor job openings:
Infographic showing various Remote Data Science Tutor job openings in Quebec as of July 2026, with employment types broken down into 37% Full Time, 43% Part Time, and 20% Contract. Highlights an 100% Remote job distribution.

Consulting Senior Data Architect (Microsoft Fabric Focus)

Omm IT Solutions

Remote

Contractor

Re-posted 20 days ago


Job description

\n <\/head>\n \n

PLEASE NOTE:<\/b><\/span><\/span><\/u> <\/b><\/span><\/span>
<\/b><\/span><\/p>\n

    \n
  • It is a 100% Remote position in Canada.<\/b><\/span><\/span>
    <\/b><\/span><\/span><\/li>\n
  • Candidates Preferred from EST\/CST Time Zone.<\/b><\/span><\/span>
    <\/b><\/span><\/span><\/li>\n <\/ul>

    JOB SUMMARY:<\/b><\/span><\/span><\/u>
    <\/span><\/span><\/p>\n

    \n We are seeking a Consulting Senior Data Architect with deep, production\-grade Microsoft Fabric experience to accelerate our active rollout of Fabric for customer\-facing and revenue\-impacting digital products hosted in Azure. This is a hands\-on role responsible for designing and building Fabric artifacts (pipelines, lakehouses, eventstream\/real\-time patterns), defining data architecture standards (conceptual\/logical\/physical), and implementing governance and security guardrails that scale across multiple product teams.<\/span><\/span>
    \n <\/div>\n
    \n
    \n <\/div>\n
    \n Key Responsibilities:<\/b><\/span><\/u><\/span>
    \n <\/div>

    Microsoft Fabric Enablement (Hands\-on Delivery + Standardization)<\/b><\/span><\/span>
    <\/span><\/p>\n

      \n
    • Architect and implement Fabric solutions for data engineering (Spark), Data Factory pipelines<\/b>, and real\-time analytics \/ Event streams <\/b>aligned to digital product needs.<\/span><\/span>
      <\/span><\/li>\n
    • Build and standardize Fabric patterns for ingestion, transformation, and serving across workloads including operational analytics, near\-real\-time, batch, and data science\/ML.<\/b><\/span><\/span>
      <\/span><\/li>\n
    • Create repeatable reference implementations for common digital product scenarios (IoT telemetry, time series, transactional + event fusion, documents, geospatial).<\/span><\/span>
      <\/span><\/li>\n <\/ul>

      Unified Data Platform Architecture (Target State + Roadmap)<\/b><\/span><\/span>
      <\/span><\/p>\n

        \n
      • Produce and maintain the target\-state architecture<\/b> for Fabric\-based data platform capabilities.<\/span><\/span>
        <\/span><\/li>\n
      • Define domain\-oriented data product patterns<\/b> including how shared\/enterprise datasets are curated and reused.<\/span><\/span>
        <\/span><\/li>\n
      • Establish architectural boundaries and integration guidance for shared datasets vs. product\-owned datasets.<\/span><\/span>
        <\/span><\/li>\n <\/ul>

        Data Modeling Standards (Conceptual \/ Logical \/ Physical)<\/b><\/span><\/span>
        <\/span><\/p>\n

          \n
        • Define and enforce data modeling standards and templates appropriate to Fabric Lakehouse\/Warehouse patterns and product analytics needs.<\/span><\/span>
          <\/span><\/li>\n
        • Provide modeling guidance for high\-variance data types (telemetry, geospatial, documents) and hybrid operational\-analytics use cases.<\/span><\/span>
          <\/span><\/li>\n
        • Define standards for schema evolution, versioning, and contract\-first data interfaces (where applicable).<\/span><\/span>
          <\/span><\/li>\n <\/ul>

          Governance, Security, and Compliance by Design<\/b><\/span><\/span>
          <\/span><\/p>\n

            \n
          • Design and implement a governance model covering classification, retention, lineage,<\/b> and auditability.<\/span><\/span>
            <\/span><\/li>\n
          • Ensure compliance guardrails are built into delivery patterns and operational processes to meet GDPR, ISO 27001, and data residency<\/b> requirements.<\/span><\/span>
            <\/span><\/li>\n
          • Define and enforce Fabric access controls using Entra ID, RBAC,<\/b> and workspace\-level controls<\/b> (including guidance for separation of duties and least privilege).<\/span><\/span>
            <\/span><\/li>\n <\/ul>

            CI\/CD + Infrastructure as Code (IaC) for Fabric<\/b><\/span><\/span>
            <\/span><\/p>\n

              \n
            • Define and implement a CI\/CD approach for Fabric artifacts<\/b> as the enterprise source of truth.<\/span><\/span>
              <\/span><\/li>\n
            • Establish release patterns for Fabric changes (promotion strategy, environment separation, approvals, and quality gates) aligned to platform standards.<\/span><\/span>
              <\/span><\/li>\n
            • Manage Fabric\-related platform configuration using Terraform<\/b> as the IaC approach (including reusable modules\/patterns).<\/span><\/span>
              <\/span><\/li>\n
            • Create golden path templates and guidance that product teams can adopt with minimal friction.<\/span><\/span>
              <\/span><\/li>\n <\/ul>

              Capacity Planning, Cost Model, and Chargeback\/Show back<\/b><\/span><\/span>
              <\/span><\/p>\n

                \n
              • Design Fabric capacity strategy (SKU sizing, workload isolation, scaling model) to support multiple products reliably.<\/span><\/span>
                <\/span><\/li>\n
              • Define guardrails and operational practices that reduce waste and improve predictability.<\/span><\/span>
                <\/span><\/li>\n <\/ul>

                Reliability, Observability, and Operational Readiness<\/b><\/span><\/span>
                <\/span><\/p>\n

                  \n
                • Define reliability patterns and operational standards for data pipelines and real\-time workloads.<\/span><\/span>
                  <\/span><\/li>\n
                • Integrate logging\/monitoring with Log Analytics<\/b> and security monitoring with Sentinel,<\/b> including alerting and incident response considerations.<\/span><\/span>
                  <\/span><\/li>\n
                • Define and operationalize data quality SLAs<\/b> (freshness, completeness, accuracy, timeliness) and embed quality checks into delivery pipelines.<\/span><\/span>
                  <\/span><\/li>\n <\/ul>

                  Consulting Engagement + Governance Forums<\/b><\/span><\/span>
                  <\/span><\/p>\n

                    \n
                  • Participate in architectural governance and provide architecture review\/sign\-off, with authority to mandate standards<\/b> when necessary to protect platform integrity<\/span><\/span>
                    <\/span><\/li>\n
                  • Partner closely with platform engineering to align patterns across identity, network, DevOps, and security.<\/span><\/span>
                    <\/span><\/li>\n <\/ul>

                    Working Style & Mindset<\/b><\/span><\/span>
                    <\/span><\/p>\n

                      \n
                    • Hands\-on architect:<\/b> you can design <\/span>and<\/span><\/i> build the critical Fabric artifacts to prove patterns.<\/span><\/span>
                      <\/span><\/li>\n
                    • Platform\-oriented:<\/b> you think in reusable standards, templates, and repeatable governance.<\/span><\/span>
                      <\/span><\/li>\n
                    • Strong consultative presence:<\/b> you can advise product teams while also driving decisions and outcomes.<\/span><\/span>
                      <\/span><\/li>\n
                    • Comfortable with authority:<\/b> you can mandate standards when required to protect the platform and business.<\/span><\/span>
                      <\/span><\/li>\n
                    • Documentation discipline:<\/b> you produce clear ADRs, standards, and operating playbooks.<\/span><\/span>
                      <\/span><\/li>\n <\/ul>

                      Engagement & Collaboration<\/b><\/span><\/span>
                      <\/span><\/p>\n

                        \n
                      • Supports product teams through office hours<\/b> and project\-based sprints.<\/b><\/span><\/span>
                        <\/span><\/li>\n
                      • Works primarily with: Azure lead architect, security architect\/engineer, DevOps platform engineer, and product engineering teams.<\/span><\/span>
                        <\/span><\/li>\n
                      • Data ownership remains with product teams;<\/b> this role defines the how (standards\/patterns\/governance), not centralized ownership.<\/span><\/span>
                        <\/span><\/li>\n <\/ul>\n
                        \n
                        <\/span>\n <\/div><\/span>
                        Requirements<\/h3>

                        Required Deliverables:<\/b><\/span><\/u><\/span>
                        <\/p>

                        You will be accountable for producing the following:<\/span><\/span>
                        <\/span><\/p>\n

                          \n
                        • Target\-state architecture <\/b><\/span><\/span>
                          <\/span><\/li>\n
                        • Data model standards:<\/b> conceptual \/ logical \/ physical + templates<\/span><\/span>
                          <\/span><\/li>\n
                        • Domain\-oriented data product patterns<\/b> and operating guidance<\/span><\/span>
                          <\/span><\/li>\n
                        • Governance model:<\/b> classification, retention, lineage, access controls<\/span><\/span>
                          <\/span><\/li>\n
                        • Fabric workspace strategy + operating model<\/b> (environments, isolation, ownership, lifecycle)<\/span><\/span>
                          <\/span><\/li>\n
                        • CI\/CD approach for Fabric artifacts<\/b> integrated with platform guardrails<\/span><\/span>
                          <\/span><\/li>\n
                        • IaC approach using Terraform<\/b> for Fabric\-related configuration and<\/span><\/span>
                          <\/span><\/li>\n
                        • Cost model + capacity planning strategy<\/b> (SKU sizing, isolation, show back\/chargeback)<\/span><\/span>
                          <\/span><\/li>\n
                        • Architecture Decision Records (ADRs)<\/b> for key platform decisions<\/span><\/span>
                          <\/span><\/u><\/li>\n <\/ul>

                          Qualifications:<\/b><\/span><\/u><\/span>
                          <\/p>

                          Required Experience & Skills<\/b><\/span><\/span>
                          <\/span><\/p>\n

                            \n
                          • 10+ years<\/b> in data architecture\/data engineering roles, including platform\-scale design.<\/span><\/span>
                            <\/span><\/li>\n
                          • Proven Microsoft Fabric production implementations<\/b> <\/b>you have delivered Fabric solutions that run in production with real operational constraints.<\/span><\/span>
                            <\/span><\/li>\n
                          • Deep hands\-on expertise in Fabric areas central to our rollout:<\/span><\/span>
                            <\/span><\/li>\n
                              \n
                            • Data Engineering <\/b><\/span><\/span>
                              <\/b><\/span><\/li>\n
                            • Data Factory (pipelines)<\/b><\/span><\/span>
                              <\/b><\/span><\/li>\n
                            • Real\-time analytics \/ Event Streams<\/b><\/span><\/span>
                              <\/span><\/li>\n <\/ul>\n
                            • Strong architecture capability across mixed data types: transactional, telemetry\/events, documents, geospatial, time series.<\/span><\/span>
                              <\/span><\/li>\n
                            • Demonstrated experience implementing and governing:<\/span><\/span>
                              <\/span><\/li>\n
                                \n
                              • Data modeling standards (conceptual\/logical\/physical)<\/span><\/span>
                                <\/span><\/li>\n
                              • Data governance (classification, retention, lineage)<\/span><\/span>
                                <\/span><\/li>\n
                              • Security patterns using Entra ID<\/b>, RBAC, workspace\-level controls<\/span><\/span>
                                <\/span><\/li>\n
                              • Compliance guardrails for GDPR, ISO 27001,<\/b> and data residency<\/b><\/span><\/span>
                                <\/span><\/li>\n <\/ul>\n
                              • Strong DevOps fluency:<\/span><\/span>
                                <\/span><\/li>\n
                                  \n
                                • CI\/CD patterns and operational delivery<\/span><\/span>
                                  <\/span><\/li>\n
                                • Terraform<\/b> as mandatory IaC for repeatability and standardization<\/span><\/span>
                                  <\/span><\/li>\n <\/ul>\n
                                • Ability to define standards and enforce guardrails<\/b> while maintaining a delivery\-first, pragmatic approach.<\/span><\/span>
                                  <\/span><\/li>\n <\/ul>

                                  Preferred<\/u><\/b><\/span><\/span>
                                  <\/span><\/p>\n

                                    \n
                                  • Experience supporting IoT<\/b> and telemetry\-heavy product ecosystems.<\/span><\/span>
                                    <\/span><\/li>\n
                                  • Experience designing data quality frameworks and SLAs for operational analytics and near\-real\-time processing.<\/span><\/span>
                                    <\/span><\/li>\n
                                  • Familiarity integrating observability\/security signals into Log Analytics<\/b> and Sentinel.<\/b><\/span><\/span>
                                    <\/span><\/li>\n <\/ul>\n
                                    \n
                                    <\/span>\n <\/div><\/span>
                                    \n <\/body>\n<\/html>