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Data Science Jobs in Saskatchewan (NOW HIRING)

Supervisor - Hydrogeology

Saskatoon, SK · On-site +1

CA$120K - CA$160K/yr

Technical communications of groundwater data including data analysis and report writing. * Study ... Bachelor's degree in related science (geology, chemistry, environmental science or engineering)

CA$22/hr

... data from various source materials (textbooks, atlases, written descriptive publications); possess ... Required: ArcGIS/ArcMap (10.0) GEOG 201 Introductory Geo-Information Sciences GEOG 307 ...

New

CA$22/hr

... data from various source materials (textbooks, atlases, written descriptive publications); possess ... Required: ArcGIS/ArcMap (10.0) GEOG 201 Introductory Geo-Information Sciences GEOG 307 ...

New

This role advises internal and external clients on statistical planning, study design, data ... Demonstrates thought leadership though contributions to website content and scientific publications ...

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Showing results 1-20

Data Science information

See Saskatchewan salary details

$23.5K

$116.9K

$210.5K

How much do data science jobs pay per year?

As of Jun 17, 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.

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 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:

Senior Developer (Lending Data Engineering)

Farm Credit Canada

Regina, SK

Full-time

Posted 3 days ago


Job description

Closing Date (MM/DD/YYYY):

06/26/2026

Worker Type:

Permanent

Language(s) Required:

English

Term Duration (in months):

Salary Range (plus eligible to receive a performance based incentive, applicable to position) :

$107,780 - $145,820

Why FCC?

At FCC, we’re proud to be 100% invested in Canadian agriculture and food. As a federal Crown corporation, we provide financing, knowledge resources and business management software to over 103,000 customers nationwide.

Here’s what you can expect when you join our team:

  • Competitive total rewards packages: market-aligned and performance-based salary and incentive programs, flexible and comprehensive group benefit and savings plans, and well-being support through benefits and wellness programs

  • Purpose-driven work: We build strong relationships, share knowledge and support the people who feed the world

  • Growth: Learning and development opportunities to help you thrive

  • Hybrid work options

~

How you'll make an impact

Put your engineering expertise into practice by solving complicated problems in our internal lending software by building reliable and modern data pipelines and making a meaningful impact through both technical leadership and collaboration.

As a Senior Developer, you’ll play a critical role in ensuring FCC’s lending data is trusted, accurate, timely, and ready to drive decision-making across the organization. As a senior technical contributor and informal technical lead, you will design and build modern, cloud-based data pipelines that move loan-related data from lending systems into the enterprise Business Vault using Data Vault 2.0 methodologies. Your work will directly enable high‑quality reporting, risk analysis, and financial insights that support FCC’s business and strategic objectives.

In this role, you’ll collaborate closely with product owners, analysts, architects, and other developers to design end‑to‑end data solutions, while also remaining hands‑on with coding, testing, and deployment. Beyond delivery, you’ll guide and mentor other developers by sharing best practices, influencing architectural decisions, and promoting strong engineering and data development standards.

What you'll do

  • Develop, test, and deploy high‑quality code using Python, PySpark, and SQL in a cloud‑based environment

  • Lead technical and solution design session within an agile development team

  • Influence and champion the vision for data engineering across several Lending teams

  • Use modern data engineering practices and Data Vault 2.0 methodologies to design, build, and maintain ETL data pipelines

  • Ensure technical solutions, pipelines and data models are fit for purpose, aligned with enterprise standards, and maintained in the most efficient and effective manner

  • Collaborate closely with product owners, analysts, architects, and internal data consumers across Reporting, Risk, and Finance.

  • Act as an informal technical leader by mentoring and coaching developers, leading code reviews and providing constructive feedback to team members

  • Improve pipeline reliability and maturity by enhancing automation, integration testing, and CI practices across the data development lifecycle

What you'll bring to the team

Required qualifications

  • Bachelor’s degree in computer science or software engineering

  • Minimum 10 years of software development experience (or an equivalent combination of education and experience)

  • Expert-level proficiency in some or all of the following AWS technologies: AWS Glue, Step Functions, S3, Redshift, Quicksight, Lambda, Eventbridge, Sagemaker, and AppFlow

  • Expert level proficiency in Python and/or PySpark

  • Extensive experience designing and building ETL data pipelines

  • Data modelling experience in both Data Vault 2.0 and Dimensional (Star Schema) methodologies

  • Proven knowledge of common design patterns and when to apply them, such as object-oriented programming (OOP) and functional programming (FP) paradigms

  • Experience optimizing design to enable business value creation

  • Demonstrated experience with application design and system integration

  • Demonstrated experience with modern approaches to code reviews, version control and continuous integration

  • Foundational knowledge of financial services concepts such as loans, credit scores, and risk data

Preferred qualifications

  • Experience coaching and mentoring peers (informal leadership)

  • Experience building automation and integration tests and designing easy-to-use frameworks

  • Working knowledge of Typescript

Not sure you meet every requirement? We encourage you to apply anyway.

#FCCEN

You belong here
At FCC, we’re committed to creating an inclusive, equitable and accessible workplace - one that reflects the communities where we live, work and play. Our team is made stronger through diversity, and we’re dedicated to building a workforce that brings together a range of backgrounds, abilities and perspectives.
We encourage qualified applicants to apply, including members of these four employment equity groups:
• Indigenous Peoples
• Members of visible minority groups
• Persons with disabilities
• Women

Accessibility and accommodations

To support an inclusive and accessible candidate experience, we encourage anyone needing an adjustment or accommodation during any stage of the recruitment process to email us at: . An HR partner will respond and work with applicants who request a reasonable accommodation. Information received in relation to accommodation requests will not impact hiring decisions.