Data Scientist Job Edmonton area,Alberta Canada,IT/Tech

Join Servus Credit Union
- Canada’s Largest Credit Union!

We are hiring for a full-time Data Scientist to join our Data Analytics
Team!

Posting closes: March 13, 2026

Employment Status:Full-time;
Remote First - with occasional travel to Vancouver or Edmonton

Additional Information: Candidate must reside, and be legally eligible to work, in Canada

For Internal Use Only/Role Grade: P4; C

At Servus Credit Union
, we do things differently—because we’re a credit union, not a bank. And that difference matters.

We believe in putting people before profits
, which is why our members are also our owners. Every dollar we earn is reinvested into the things that matter most:
our members, our employees, and our communities
. Whether it’s through profit sharing, competitive rates, or local initiatives that make a real impact, we’re here to help Albertans feel good about their money.

We’re looking for passionate, values-driven people to join our team. We welcome individuals from all backgrounds who are aligned with our values and are eager to contribute in meaningful ways. If you're excited to grow your career with a team that’s innovative, hardworking, collaborative and deeply rooted in community,
we want to meet you
.

Join Servus and discover a better way to work.

As a Data Scientist, reporting to the Director Data Science, you’ll play a key role in helping the credit union achieve its strategic objectives through advanced analytics. You will design and implement innovative data-driven solutions to attract and retain members, enhance their experience, and build long-term loyalty. This role involves modeling complex problems, developing predictive systems, and uncovering opportunities using advanced analytics, machine learning, and data visualization techniques.

What You'll be Doing:

Advanced Analytics: Analyze a wide range of structured and unstructured data sets—including transactional, behavioral, social media, conversational, and survey data—to generate actionable insights.

Predictive Modeling: Apply cutting-edge techniques such as machine learning, text and data mining, clustering, classification, regression, and discrete choice modeling to solve complex business problems.

Data Integration: Combine internal member data with third-party sources to develop statistical models of behavior, experience, and brand health.

Segmentation and Strategy: Drive refined segmentation strategies including persona development, market and branch profiling, profitability analysis, and channel optimization.

Project Ownership: Lead end-to-end data science projects, from defining the scientific approach and project plan to delivering insights and solutions aligned with business goals.

Stakeholder

Collaboration:

Partner with business leaders to understand challenges, define KPIs, and translate business needs into analytical solutions.

Continuous Improvement: Stay current with evolving data science tools and methodologies to enhance capabilities and outcomes.

What you Bring:

Required Qualifications and Experience

Graduate degree in a quantitative field such as Statistics, Mathematics, Econometrics, Engineering, or equivalent practical experience.

Relevant certifications (, Databricks Machine Learning Associate or Professional, Lakehouse Fundamentals, Microsoft Azure Data Fundamentals).

Hands-on experience in building, deploying, and managing predictive models using agile and MLFlow-based methodologies.

Proficiency and experience working within Databricks and Microsoft Azure environments.

Experience working within a Databricks ecosystem is desirable.

Key Competencies

Communication: Confident public speaker with the ability to clearly convey complex analytical concepts to non-technical stakeholders.

Analytical Thinking: Strong problem-solving skills with a track record of delivering innovative solutions.

Collaboration: A team player who can build relationships across functions and contribute to a culture of cooperation.

Adaptability: Comfortable in a dynamic environment with shifting priorities and emerging technologies.

Leadership: Capable of leading projects and guiding others through the data science life cycle.

Detail Orientation: Rigorous attention to…


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