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Intern Data Scientist Machine Learning Jobs in Edmonton, AB

National Bank

Senior Analyst

Edmonton, AB · On-site

... Science and Data Science. * Completion or progression in CFA and/or FRM would be an asset ... Knowledge of AI and Machine Learning would be an asset. * Excellent analytical skills to identify ...

Hubbell Incorporated

Sr Research Engineer

Edmonton, AB · On-site

Research activities span sensors, controls, communications, analytics (including machine learning ... Conduct scientific computing experiments, numerical simulations, and technical analyses to support ...

Promise Robotics

Software Developer

Edmonton, AB

Experience with machine learning techniques and frameworks (e.g., RL, CNNs, RNNs, Keras ... Competitive salary: we believe in data-driven, equitable compensation decisions. We recently ...

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Intern Data Scientist Machine Learning information

What does an intern data scientist machine learning do?

An Intern Data Scientist in Machine Learning assists in analyzing large datasets, building predictive models, and extracting insights to support business decisions. They often work under the guidance of experienced data scientists to clean data, implement machine learning algorithms, and evaluate model performance. Their responsibilities may also include data visualization and reporting findings to team members. This role provides hands-on experience with real-world data science problems and tools, helping interns develop essential technical and analytical skills.

What types of projects and responsibilities can an intern data scientist machine learning expect to work on?

As an Intern Data Scientist focused on Machine Learning, you will often assist in tasks such as data cleaning, feature engineering, and developing or testing machine learning models under the supervision of senior team members. You may also be involved in exploratory data analysis and help interpret model results to provide actionable insights. Interns typically collaborate closely with data engineers, analysts, and software developers, gaining exposure to end-to-end machine learning pipelines. This hands-on experience provides valuable learning opportunities and helps build the foundational skills needed for future roles in data science.

What are the key skills and qualifications needed to thrive as an intern data scientist machine learning, and why are they important?

To thrive as an Intern Data Scientist (Machine Learning), you need a solid understanding of statistics, programming skills (typically in Python or R), and foundational knowledge of machine learning algorithms, often supported by coursework or relevant projects. Familiarity with tools like scikit-learn, TensorFlow, Jupyter notebooks, and version control systems (e.g., Git) is commonly expected. Strong analytical thinking, curiosity, and effective communication skills help you interpret data insights and work collaboratively within a team. These abilities are crucial for translating data into actionable solutions and contributing to impactful machine learning projects.

What is the difference between Intern Data Scientist Machine Learning vs Intern Data Analyst?

AspectIntern Data Scientist Machine LearningIntern Data Analyst
Required SkillsBasic programming, statistics, machine learning conceptsData analysis, Excel, SQL, visualization tools
Work EnvironmentResearch-focused, model development, algorithm testingData cleaning, reporting, dashboard creation
Common Industry UsageTech, finance, healthcareRetail, marketing, finance

Intern Data Scientist Machine Learning roles focus on developing and testing machine learning models, requiring knowledge of algorithms and programming. Intern Data Analyst positions emphasize data cleaning, analysis, and visualization. Both roles are entry-level but differ in technical depth and project focus, catering to different career paths within data-driven industries.

What are popular job titles related to Intern Data Scientist Machine Learning jobs in Edmonton, AB?

For Intern Data Scientist Machine Learning jobs in Edmonton, AB, the most frequently searched job titles are:

What job categories do people searching Intern Data Scientist Machine Learning jobs in Edmonton, AB look for?

The top searched job categories for Intern Data Scientist Machine Learning jobs in Edmonton, AB are:

Intern Data Scientist Machine Learning jobs near you
Infographic showing various Intern Data Scientist Machine Learning job openings in Edmonton, AB as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Data Engineer - Senior (REMOTE) JP991

P@thlion Staffing Careers

Edmonton, AB • Remote

Full-time

Posted 10 days ago

Job description

Project Name:

Digital Regulatory Assurance System

Scope:

Modernization initiatives across the Government of Alberta are fundamentally changing how ministry users collect, manage, analyze, and use data as legacy systems are transformed into modern Data Management and Geospatial Platforms. This shift requires dedicated analytical capacity to ensure that the value of modernized data assets is fully realized.

DRAS is a Government of Alberta regulatory transformation initiative led by Environment and Protected Areas (EPA) to modernize, digitize, and streamline environmental and natural resource regulatory processes. DRAS supports the full regulatory lifecycle, from application and authorization to monitoring, compliance, remediation, and closure through a single, consolidated digital platform

As DRAS development continues, the volume, variety, and complexity of structured data continue to grow, creating a sustained need for dedicated data engineering and data product expertise. The Data Product Analyst role is critical to ensuring that modernization delivers tangible business value. This role will design, build, and operate reliable data pipelines that ingest and integrate data into the DMP, apply standardized transformations, enforce data quality and governance controls, and produce trusted, analytics‑ready datasets that support regulatory oversight, compliance monitoring, and evidence‑based decision‑making aligned with DRAS objectives.

The Data Product Analyst provides operational support and continuous improvement (CI) services for DRAS data products and platform solutions. The role focuses on ensuring the ongoing reliability, availability, and performance of DRAS data assets, integrations, reports, and analytics products.

Duties:

  • Collaborate with business stakeholders and product owners to understand data product objectives, requirements, and success criteria
  • Design and implement scalable, secure, and high-performance data architecture on Microsoft Azure, supporting both cloud-native and hybrid environments.
  • Lead the development of data ingestion, transformation, and integration pipelines using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.
  • Work with the Data Architect and manage data lakes and structured storage solutions using Azure Data Lake Storage Gen2, ensuring efficient access and governance.
  • Integrate data from diverse source systems including ServiceNow, and geospatial systems, using APIs, connectors, and custom scripts.
  • Develop and maintain robust data models and semantic layers to support operational reporting, analytics, and machine learning use cases and downstream consumption.
  • Build and optimize data workflows using Python and SQL for data cleansing, enrichment, and advanced analytics within Azure Databricks.
  • Design and expose secure data services and APIs using Azure API Management for downstream systems.
  • Implement data governance practices, including metadata management, data classification, and lineage tracking.
  • Ensure compliance with privacy and regulatory standards (e.g., FOIP, GDPR) through role-based access controls, encryption, and data masking.
  • Monitor and troubleshoot data pipelines and integrations, ensuring reliability, scalability, and performance across the platform.
  • Utilize AI and automation tools to streamline data engineering workflows, including pipeline development, testing, monitoring, and documentation.
  • Leverage AI-assisted tools for code generation, optimization, and review to improve development efficiency and code quality.
  • Design and curate standardized, high‑quality datasets that are suitable for advanced analytics and future AI use cases.
  • Other duties as needed