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Data Science Jobs in Alberta (NOW HIRING)

As a Machine Learning Specialist on the team, you will combine your expert knowledge of data science with your strong ML Ops and software development skills to automate and facilitate data ...

As a Machine Learning Specialist on the team, you will combine your expert knowledge of data science with your strong ML Ops and software development skills to automate and facilitate data ...

The Opportunity We're hiring a Lead Data Scientist to anchor one of the most important bets in our next chapter. Fullscript is building intelligence trained on real-world functional outcomes. Not ...

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or Business. * Prior consulting or practice leadership experience at a global technology or professional services firm.

You will work closely with a team of data scientists and data engineers to deploy solutions and drive innovation using machine learning and NLP techniques. The ideal candidate has a deep ...

You will work closely with a team of data scientists and data engineers to deploy solutions and drive innovation using machine learning and NLP techniques. The ideal candidate has a deep ...

You will operate across pipeline architecture, platform design, and cross-functional data strategy, partnering closely with product, data science, and engineering leadership. Why This Job is Exciting

Minimum 8+ years of experience in data or analytics roles, supported by formal education in Business, Economics, Data Science, or a related field * Experience in Canadian and U.S. crude and refined ...

You will operate across pipeline architecture, platform design, and cross-functional data strategy, partnering closely with product, data science, and engineering leadership. Why This Job is Exciting

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Data Science information

See Alberta salary details

$23.5K

$116.9K

$210.5K

How much do data science jobs pay per year?

As of Jun 11, 2026, the average yearly pay for data science in Alberta is $116,864.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,000.00 and $161,000.00 per year, depending on experience, location, and employer.

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

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

Is 40 too late for data science?

Data science is a field open to individuals of all ages, and many professionals transition into it later in their careers. Success often depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be learned through online courses, bootcamps, or degrees regardless of age.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

Is AI replacing data scientists?

AI is transforming the role of data scientists by automating routine tasks such as data cleaning and basic analysis, but it does not replace the need for skilled professionals to interpret complex data, develop models, and make strategic decisions. Data scientists with expertise in programming, statistical analysis, and machine learning remain essential for designing and deploying AI solutions effectively.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What jobs are there in data science?

Data science offers a variety of roles including Data Scientist, Data Analyst, Machine Learning Engineer, Data Engineer, and Business Intelligence Analyst. These positions typically require skills in programming, statistics, and data visualization tools, and may involve working with large datasets, predictive modeling, and data-driven decision making.

What Does a Data Scientist Do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.

What jobs does a data scientist do?

A data scientist analyzes large datasets to extract insights, build predictive models, and support decision-making. They use programming languages like Python or R, employ statistical techniques, and often work with machine learning algorithms to solve complex problems across various industries.
What are the most commonly searched types of Data Science jobs in Alberta? The most popular types of Data Science jobs in Alberta are:
What are popular job titles related to Data Science jobs in Alberta? For Data Science jobs in Alberta, the most frequently searched job titles are:
What job categories do people searching Data Science jobs in Alberta look for? The top searched job categories for Data Science jobs in Alberta are:
What cities in Alberta are hiring for Data Science jobs? Cities in Alberta with the most Data Science job openings:
Infographic showing various Data Science job openings in Alberta as of June 2026, with employment types broken down into 1% As Needed, 86% Full Time, 12% Part Time, and 1% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $116,864 per year, or $56.2 per hour.

Staff Applied AI/ML Scientist

TELUS

Edmonton, AB • On-site

Other

Posted 4 days ago


TELUS rating

8.0

Company rating: 8.0 out of 10

Based on 9 frontline employees who took The Breakroom Quiz

18th of 76 rated telecommunications companies


Job description

Join our team and what we'll accomplish together

The AI Accelerator team is on a continuous journey towards helping TELUS become a world-class leader in data solutions, doing so by delivering data analytics capabilities built upon unified scalable platforms, advanced AI solutions, high-quality data, and a data-product-oriented culture while always keeping an eye on the horizon, preparing for the next big thing. We are entrepreneurial and live by our AI Manifesto of failing fast and being outcome vs technology-driven, creating value for our customers, team members, communities, and the environment. The team takes pride in our Artificial Intelligence and Machine Learning capabilities and takes ownership of each step of the process. From hypothesis generation, initial exploring of datasets, developing novel AI techniques to discover insights, to developing automation pipelines and web visualizations, we do it all!

 
Always wanted to work with a team of innovators touching all business units within TELUS, and be part of a culture that embraces creativity and collaboration? If so, we'd love to talk with you!

 
You'll be a part of the team and journey that will transform the way we do business across various domains. You'll collaborate with teams across the company, seeking out various data sources to help identify new business opportunities while championing data-driven decision-making and the accelerated adoption of AI. As a Machine Learning Specialist on the team, you will combine your expert knowledge of data science with your strong ML Ops and software development skills to automate and facilitate data exploration, analytics, machine learning model development, training and deployment and will leverage your experience in building reusable algorithms, functions and libraries.

What you'll do
  • Lead the iterative development, validation, and deployment of AI/ML models across the organization, ensuring continuous improvement and scalability
  • Drive the development and deployment of Generative AI applications, tools, and frameworks that solve critical business challenges
  • Collaborate on end-to-end automation efforts required to bring machine learning models to production, ensuring smooth deployment and operationalization
  • Work with both structured and unstructured data to design, develop, and deploy innovative predictive models, metrics, and dashboards that deliver actionable insights
  • Visualize and report findings creatively through various formats (e.g., dashboards, interactive reports) to ensure insights are easily understood and actionable for stakeholders at all levels
  • Influence strategic decision-making and drive the adoption of a data-driven mindset across the organization by solving business challenges and uncovering new opportunities
  • Develop re-usable code and solutions to accelerate future goals and deliver results faster and more reliably
  • Support the evolution of Data science and AI products by influencing product roadmap through prioritizing features to meet business needs, recommending latest industry research, tools and best practices
  • Build and maintain strong engagement with key stakeholders, understanding their business needs and priorities, and presenting AI/ML initiatives to VP-level executives and beyond
  • Serve as a functional leader of AI/ML across the company, providing technical leadership for the overall AI/ML program and collaborating with broader business units to scale AI/ML solutions
  • Establish best practices for ML model deployment and production, ensuring models are scalable, reliable, and optimized for long-term success
  • Represent the company as a thought leader in the AI/ML space, presenting at conferences, publishing papers, and engaging with external forums to build TELUS's reputation as a global leader in data science and AI
  • Coach and mentor a growing team of data scientists and engineers, fostering a culture of continuous learning and innovation, and identifying future leaders within the organization
What you bring
  • Minimum 7- 10 years of hands-on experience in machine learning, AI, and data analysis including deployment of solutions in business workflows
  • Strong expertise in Python and experience with data science libraries (e.g., Scikit-learn, Pandas, Numpy)
  • Solid background in machine learning algorithms, including regression, classification, clustering, time series analysis, Reinforcement learning and optimization
  • Experience building and deploying GenAI applications and workflows
  • Proficiency in SQL and distributed computing
  • Hands-on experience with cloud platforms such as GCP, AWS, or Azure
  • Expertise in machine learning frameworks such as TensorFlow, Pytorch, and Keras
  • Strong understanding of software and AI development lifecycles, with experience in DevOps and MLOps practices
  • Understanding of version control systems (e.g., Git) for collaborative development
  • Ability to communicate complex technical concepts to non-technical audiences effectively
  • A fast-moving, agile approach to removing roadblocks and delivering results quickly

Great-to-haves

  • Masters or PhD degree in a quantitative field such as Math, Statistics, Computer Science, Economics, Engineering, or Data Science
  • Experience with agile methodology and work in a start-up environment 
  • GCP or other cloud certifications
  • 10+ years of experience in Data Science; at least 5 years of experience in independently leading projects/modeling; at least 2 years leading a major functional area
  • Knowledge of change management practice to advance the adoption of technology solutions in the business workflow

Advanced knowledge of English is required because you will most of the time interact in English with external parties (clients, suppliers, candidates, external partners, etc.); interact in English with internal parties (colleagues, internal partners, stakeholders, etc.); and work with IT tools whose interface is only accessible in English as part of this position's main responsibilities given its international scope.