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Data Science Jobs in Saskatchewan (NOW HIRING)

Vendasta

Forward Deployed Engineer

Saskatoon, SK · On-site

Bachelor's Degree in Computer Science, Software Engineering, Computer Engineering, Data Science, or a closely related quantitative field (or equivalent practical experience). * 5+ years of software ...

New

Vendasta

Forward Deployed Engineer

Saskatoon, SK · On-site

Bachelors Degree in Computer Science, Software Engineering, Computer Engineering, Data Science, or a closely related quantitative field (or equivalent practical experience). * 5+ years of software ...

New

Vendasta

Forward Deployed Engineer

Saskatoon, SK · On-site

Bachelor's Degree in Computer Science, Software Engineering, Computer Engineering, Data Science, or a closely related quantitative field (or equivalent practical experience). * 5+ years of software ...

New

Viterra

Data Center Server Specialist

Regina, SK

Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, or ... data centers. * VMware Certified Advanced Professional (VCAP) or VMware Certified Design Expert ...

Bunge

Data Center Server Specialist

Regina, SK · On-site +1

Bachelor's or Master's degree in Computer Science, Computer Engineering, Information Technology, or ... data centers. * VMware Certified Advanced Professional (VCAP) or VMware Certified Design Expert ...

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All JobsData Science JobsData Science Jobs in Saskatchewan

Data Science information

See Saskatchewan salary details

$23.5K

$116.9K

$210.5K

How much do data science jobs pay per year?

As of Aug 9, 2026, the average yearly pay for data science in Saskatchewan 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.

Is a data scientist in high demand?

Yes, data scientists are in high demand across many industries due to the increasing reliance on data-driven decision making. The role requires skills in programming, statistics, and machine learning, and job growth is expected to continue as organizations expand their data capabilities.

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.

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.

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 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.
More about Data Science jobs
What are the most commonly searched types of Data Science jobs in Saskatchewan? The most popular types of Data Science jobs in Saskatchewan are:
What are popular job titles related to Data Science jobs in Saskatchewan? For Data Science jobs in Saskatchewan, the most frequently searched job titles are:
What job categories do people searching Data Science jobs in Saskatchewan look for? The top searched job categories for Data Science jobs in Saskatchewan are:
What cities in Saskatchewan are hiring for Data Science jobs? Cities in Saskatchewan with the most Data Science job openings:
Data Science jobs near you
Infographic showing various Data Science job openings in Saskatchewan as of August 2026, with employment types broken down into 82% Full Time, 12% Part Time, and 6% Temporary. Highlights an 82% In-person, 6% Hybrid, and 12% Remote job distribution, with an average salary of $116,864 per year, or $56.2 per hour.

Remote Materials Scientist / Engineer

Micro1

Regina, SK • Remote

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Materials Scientist / Engineer
$80 - $130/hourpay
Required Skills
materials science expertise
materials characterization
technical literature review
scientific data interpretation
clear written and verbal communication
remote collaboration
annotating technical datasets
scenario and case study development
quality assurance in scientific deliverables
analytical skills
About micro1
micro1 is the leading AI data lab for training frontier models and evaluating AI agents. Experts contribute their diverse subject matter knowledge across domains such as finance, healthcare, STEM engineering, and more. micro1 transforms that real-world expertise into high-quality training data, evaluations, and feedback loops that improve how AI systems learn, reason, and perform.

Our platform identifies and vets top talent through an AI recruiter, enabling high-quality expert contributions at scale. We aim to enable 1 billion people to do meaningful work by applying their expertise to AI. As our global expert network grows, micro1 is building the human intelligence layer for frontier AI.

Role Title: Materials Scientist / Engineer


Role Type: Contractor


Location: Remote


micro1 is engaging Materials Scientists / Engineers to contribute their technical expertise to a customer’s advanced materials project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Analyze and interpret scientific data from experimental results, technical datasets, and research publications in materials science and engineering.
  2. Conduct comprehensive technical literature reviews to identify key advances, methodologies, and challenges in materials characterization and related fields.
  3. Annotate and structure technical datasets with precise, detailed commentary and context to support AI model training.
  4. Develop realistic scenarios and case studies that represent practical applications and challenges within materials engineering, metallurgy, or related sectors.
  5. Provide clear, well-organized written and verbal explanations of materials phenomena, properties, and scientific reasoning.
  6. Review and assure the quality, consistency, and accuracy of scientific deliverables submitted for project milestones.
  7. Collaborate remotely with other scientific contributors, leveraging digital tools and documentation practices.


Preferred Qualifications

  1. MS or PhD in Materials Science & Engineering, Metallurgy, Mechanical Engineering, Chemical Engineering, or a related discipline with a materials specialization.
  2. Demonstrated expertise in materials characterization techniques (e.g., microscopy, spectroscopy, mechanical testing).
  3. Strong analytical skills and experience interpreting complex scientific data.
  4. Substantial experience conducting technical literature reviews and summarizing key findings.
  5. Proven ability to communicate complex technical concepts clearly, both in writing and verbally, to diverse audiences.
  6. Experience with quality assurance and review of scientific documents or datasets.
  7. Familiarity with remote collaboration tools and digital knowledge-sharing environments.


Compensation Structure

Compensation is output-based; experts are paid per task that meets the project specifications. The time required to complete work may vary depending on the expert’s experience and workflow. Minimum submission requirements apply. Experts must submit a minimum of tasks per week.


Start Timeline & Availability

We typically fill roles within 48 hours and are looking for experts ready to jump in right away. If selected, we expect you to start your first tasks within 24–48 hours of completing onboarding.


Apply