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Intern Python Data Analyst Jobs in Alberta (NOW HIRING)

Description The Opportunity We are looking for a proactive, growth-minded Senior Data Analyst / ML ... SQL (Expert), Python * Data Warehouse: Google BigQuery * Cloud Infrastructure: Google Cloud ...

Description The Opportunity We are looking for a proactive, growth-minded Senior Data Analyst / ML ... SQL (Expert), Python * Data Warehouse: Google BigQuery * Cloud Infrastructure: Google Cloud ...

As part of the interview process for this role there will be a mandatory technical test of Python ... Collect, clean, analyze, and interpret data to identify trends, patterns, and create actionable ...

Senior Data Analyst Olsen Consulting specializes in the implementation and optimization of ... SQL, Python, Azure and knowledge of web development frameworks such as Django and Flask ...

As Manager, Information Services, you'll lead a multidisciplinary team of data engineers, analysts ... Strong technical proficiency in SQL, Python, data modeling, and cloud environments (preferably ...

As Manager, Information Services, you'll lead a multidisciplinary team of data engineers, analysts ... Strong technical proficiency in SQL, Python, data modeling, and cloud environments (preferably ...

Senior Data Scientist, ASR

Edmonton, AB · On-site

CA$77K - CA$117K/yr

Clearly communicate technical analysis and results to stakeholders using data visualizations ... Expertise in Python data science libraries like Pandas, matplotlib, NumPy, and Scikit-Learn.

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Intern Python Data Analyst information

What are the key skills and qualifications needed to thrive as an Intern Python Data Analyst, and why are they important?

To thrive as an Intern Python Data Analyst, you need a solid understanding of data analysis concepts, proficiency in Python, and familiarity with statistics, typically supported by coursework in data science or a related field. Experience using tools like pandas, NumPy, Jupyter Notebook, and SQL, as well as exposure to data visualization libraries such as matplotlib or seaborn, is highly beneficial. Curiosity, attention to detail, and strong problem-solving and communication skills help you extract insights and present findings effectively. These skills are important for accurately analyzing data, translating results into actionable insights, and supporting data-driven decisions within an organization.

What types of projects and tasks can an Intern Python Data Analyst expect to work on during their internship?

As an Intern Python Data Analyst, you can expect to work on a variety of data-driven projects, such as cleaning and preparing datasets, creating data visualizations, and running exploratory data analysis using Python libraries like pandas and matplotlib. You'll likely support senior analysts by automating data collection processes and helping to generate regular reports. Collaboration with team members from different departments is common, as you'll need to understand business needs and present your findings in a clear, actionable way. These experiences provide valuable exposure to real-world data challenges and can help you develop both technical and communication skills crucial for advancing in data analytics.

What does an Intern Python Data Analyst do?

An Intern Python Data Analyst assists in collecting, processing, and analyzing data using Python programming language. They support the data team by writing scripts to clean and visualize data, and help generate insights from large datasets. Interns also learn to use data analysis libraries such as pandas, NumPy, and matplotlib, and may assist with reporting or automation tasks. This role is typically entry-level and offers hands-on experience in data analysis within a supervised environment.

What is the difference between Intern Python Data Analyst vs Intern Data Scientist?

AspectIntern Python Data AnalystIntern Data Scientist
Required SkillsPython, SQL, Excel, Data VisualizationPython, R, Machine Learning, Statistical Analysis
Work EnvironmentData analysis, reporting, dashboardsModel development, predictive analytics, research
Industry UsageBusiness intelligence, finance, marketingTech, healthcare, research institutions

Intern Python Data Analysts focus on analyzing data, creating reports, and visualizations using Python and related tools. Intern Data Scientists work on building models, applying machine learning, and conducting advanced statistical analysis. While both roles require Python skills, Data Scientists typically need additional knowledge of R and machine learning techniques. The roles often overlap in industries like tech and finance, but Data Scientists tend to engage in more complex predictive tasks, whereas Data Analysts focus on interpreting data for business insights.

What are the most commonly searched types of Python Data Analyst jobs in Alberta? The most popular types of Python Data Analyst jobs in Alberta are:
What cities in Alberta are hiring for Intern Python Data Analyst jobs? Cities in Alberta with the most Intern Python Data Analyst job openings:

Senior Data Analyst (L4)

TELUS

Calgary, AB • On-site

Other

Posted 14 days ago


TELUS rating

8.0

Company rating: 8.0 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

19th of 76 rated telecommunications companies


Job description

Description

The Opportunity

We are looking for a proactive, growth-minded Senior Data Analyst / ML Engineer with a minimum of 5 years of experience in analyzing high-volume data and delivering strategic insights. This is a unique "growth-track" role: you will start by mastering our data landscape through advanced dashboarding and telemetry analysis, then rapidly transition into building and maintaining the predictive models that drive our customer success.

If you are someone who isn't just looking for a ticket to solve, but wants to understand the why behind the numbers to proactively prevent customer issues, you'll fit right in.

The Roadmap

  • Phase 1 (Domain Mastery & KPI Strategy): You'll dive deep into our telemetry and customer profile data. You will define the Key Performance Indicators (KPIs) that matter most and build high-impact Tableau dashboards to track them
  • Phase 2 (ML Innovation & Production): Once you've mastered the domain, you will lead the transition into predictive modeling. You will own the feature engineering process and deploy models that proactively solve customer issues

What You'll Do

  • Dashboarding & Domain Learning: Initially, you will focus on building interactive and insightful Tableau dashboards. This is your foundation to learn our domain knowledge, understand customer behavior, and identify patterns in our telemetry data
  • Feature Engineering: Architect and transform raw telemetry and customer profile data into high-signal features. You will build the data pipelines that serve as the foundation for all ML initiatives
  • ML Development & Lifecycle Management: Develop and deploy ML models (churn prediction, anomaly detection, etc.). You are responsible for maintaining models in production, including setting up automated retraining pipelines to ensure accuracy as data evolves
  • Proactive Problem Solving: Use your technical expertise to identify potential customer friction points before they become issues, moving the company from a reactive to a proactive stance
  • KPI Development & Stakeholder Presentation: Collaborate with leaders to define critical business KPIs. You will act as a data storyteller, presenting key insights to executive stakeholders and translating complex ML/Data trends into clear, strategic recommendations
Qualifications

What You Bring

  • SQL Mastery: You write clean, efficient, and complex queries at an expert level. This is the core of how you interact with our data
  • BI & Dashboarding Expertise: Proficiency in Tableau, creating interactive dashboards that drive action and help you (and the team) rapidly acquire domain knowledge
  • GCP Ecosystem: Strong experience with Google Cloud Platform, specifically BigQuery and its integration with ML tools
  • ML Ops Experience: Practical experience managing the production lifecycle, monitoring, versioning, and ensuring timely model retraining
  • The "Proactive" Edge: A strong desire to learn the business domain and grow with the company. You don't wait for instructions; you identify opportunities and solve problems before they escalate
  • Excellent Communication Skills: A proven track record of analyzing high-volume data and presenting key findings to senior-level audiences. You can explain complex technical concepts to non-technical stakeholders with ease

Preferred Qualifications (Good to Have)

  • GCP Professional Machine Learning Engineer Certification
  • Working knowledge of leveraging Claude in the workflows
  • Experience with Google Vertex AI or Kubeflow for ML orchestration
  • Background in analyzing high-volume telemetry or IoT data

Technical Stack

  • Languages: SQL (Expert), Python
  • Data Warehouse: Google BigQuery
  • Cloud Infrastructure: Google Cloud Platform (GCP)
  • Visualization: Tableau, Looker, Etc
  • ML Tools: Scikit-learn, TensorFlow/PyTorch, Vertex AI (GCP) , Claude (Anthropic)