Machine Learning Specialist - 988

A Canadian technology and workforce solutions company headquartered in Vaughan, Ontario. The company helps businesses, enterprises, and public-sector organizations modernize their operations through secure digital transformation, custom software development, staffing, recruitment, payroll, and workforce management solutions.

Title: Machine Learning Specialist

Duration: 2026-09-01 to 2027-08-31 (6 Months possible extension)

Location: Edmonton, AB (Remote) (Travel 3 -4 times)/Month

Work Location

7000 - 113 ST

Edmonton

Alberta

CAN

T6H 5T6

Project Name

Data Projects

Scope

The Data and Content Management Division uses a one-government approach to information and privacy governance, decision-making, and service delivery across the balanced with individual client needs. This facilitates enhanced data access, collaboration, reduction in data duplication, and innovation to ensure effective and efficient services across the government to provide better services to Albertans.

The Data Centre of Excellence requires a resource to support the design and development of analytical data products & services to enable the GoA to better leverage its data assets support implementation of the GoA Data Strategy. Working within a team, this resource is an expert, and plays a key role in ensuring high-quality data modeling, applying algorithms, de-identification, synthetic data creation, visualizations and development of evidence for use across ministries and with external stakeholders. This role will enact the pillars of the Data Strategy by using data to benefit Albertans

The successful candidate will be a Machine Learning Specialist with a diverse range of analytical skills and experience in working in multi-faceted roles. The role may have aspects of any or all the following: machine learning (ML) model development, artificial intelligence (AI), data analysis, data science, AI development, data engineering, data modelling, statistical analysis, &, strategizing, advising, and data product design and delivery. There may be aspects of data architecture, technical analysis, and business analysis.

Duties

Provides hands-on support, leadership, advice and direction on the strategic data initiatives that are being undertaken. A critical responsibility is to coordinate with various internal and external clients to understand their analytics needs and how to use the data to best meet these needs. The MLS creates tools, models, and analysis to apply artificial intelligence for better government policy and services. The MLS will work with cross-functional project teams to apply machine learning (ML). Services and project deliverables should evolve as the work progresses, in response to emerging user and business needs, as well as design and technical opportunities. Works with Manager Analytics Capability Centre to:

• Helps teams identify when (and when not) to apply ML, including identifying prerequisites for good ML applications.
• Facilitates, coaches, and mentors others in the application of ML to complex public challenges.
• Helps identify and select ML tools, services, and infrastructure.
• Creates data collection, normalization, and cleaning procedures.
• Creates training scripts and train models for specific domains using chosen ML packages.
• Runs ML driven analyses of large datasets and reports on findings.
• Creates ML analytics, reports, and insights to inform better services and policymaking.
• Integrates trained ML models within applications.
• Develops auditing, accountability, and transparency mechanisms for ML capabilities.
• Works within privacy legislation and provides ethical as well as practical guidance on ML implementation.
• Develop and share analytical models and products.
• Analyze and organize raw data, preparing it for prescriptive and predictive modeling, while building algorithms that deliver business value.
• Support in development of full-stack data analytics or AI applications as required.
• Provide expertise and leadership in the design and competition of analytic projects.
• Conducting complex data analysis and collaborating with data engineers and analysts on various projects.
• Bring knowledge of statistical classification techniques such as k-means and hierarchical clustering, partition trees, and logistic regression.
• Gather and document client requirements.
• Capture business and technical metadata for ML products.
• Escalate issues and risks, as appropriate.
• Work within a multi-vendor/staff environment.

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