Machine Learning Specialist - MLS 26-10262

Job Title: Machine Learning Specialist

Location: Edmonton, Alberta, Canada
Duration: 1 Year

1. Role Overview

The organization is seeking an experienced Machine Learning Specialist to support the implementation of strategic data initiatives through a coordinated, organization-wide approach to data management, governance, and utilization.

The successful candidate will work as part of a cross-functional team to support high-quality data analysis, modelling, algorithm development, data de-identification, synthetic data generation, visualization, and evidence-based decision-making.

The role focuses on developing analytical tools, models, and solutions that leverage Artificial Intelligence (AI) and Machine Learning (ML) to improve organizational services, decision-making, and policy development.

The specialist will collaborate with internal and external stakeholders to understand analytical requirements and determine how data and machine learning can best address business needs.

2. Duties and Responsibilities

The Machine Learning Specialist will provide hands-on technical support, leadership, advice, and direction for strategic data initiatives.

Key responsibilities include:

1. Help teams determine when machine learning should and should not be applied, including identifying the prerequisites for successful ML implementation.
2. Facilitate, coach, and mentor others in applying machine learning to complex organizational and public-sector challenges.
3. Identify and recommend appropriate machine learning tools, services, and infrastructure.
4. Develop data collection, normalization, and cleaning procedures.
5. Create training scripts and train machine learning models for specific domains using appropriate ML packages.
6. Conduct machine learning-driven analyses of large datasets and communicate findings.
7. Develop analytical reports, insights, and recommendations to support improved services and policymaking.
8. Integrate trained machine learning models into applications.
9. Develop auditing, accountability, and transparency mechanisms for machine learning capabilities.
10. Ensure compliance with applicable privacy legislation and provide ethical and practical guidance on ML implementation.
11. Develop, document, and share analytical models and data products.
12. Analyze and organize raw data, preparing it for prescriptive and predictive modelling.
13. Build algorithms that deliver measurable business value.
14. Support the development of full-stack data analytics and AI applications as required.
15. Provide technical expertise and leadership in the design and execution of analytical projects.
16. Conduct complex data analysis and collaborate with data engineers and analysts on various projects.
17. Apply statistical classification techniques, including K-means clustering, hierarchical clustering, partition trees, and logistic regression.
18. Gather, analyze, and document client and business requirements.
19. Capture business and technical metadata for machine learning products.
20. Identify, manage, and escalate project issues and risks as appropriate.
21. Work effectively within a multi-vendor and multidisciplinary team environment.
22. Perform other related duties and responsibilities as required.

3. Mandatory Qualifications and Experience (Must-Have) 3.1 Education

A bachelor's degree in Computer Science or a related field of study is required. Equivalent qualifications will be considered.

3.2 Programming and Statistical Languages

Experience and comprehensive skills in statistical and programming languages, such as R, Python, and SQL.

Required Experience: 6 years

3.3 Analytical and Quantitative Modelling

Experience building analytical and quantitative analysis models.

Required Experience: 6 years

3.4 Data Preparation for Predictive and Prescriptive Modelling

Experience preparing and processing data for prescriptive and predictive modelling.

Required Experience: 6 years

3.5 Data Analytics and Data Science Methodologies

Experience with and understanding of different approaches and methods for data analytics and data science, including complex statistical modelling.

Required Experience: 6 years

3.6 Artificial Intelligence and Machine Learning

Experience applying Artificial Intelligence (AI) or Machine Learning (ML) to data science or analytics.

Relevant applications include:

• Machine learning for anomaly detection.
• Predictive monitoring.
• AI-assisted data transformation.
• AI-assisted automation.

Required Experience: 6 years

4. Preferred Qualifications and Experience (Nice-to-Have) 4.1 Data Governance, Data Sharing, and Data Ethics

Experience and knowledge of data sharing, data linkage, de-identification, metadata, data quality, data ethics, synthetic data, and data literacy, including methods for promoting the effective use of organizational data.

Preferred Experience: 5 years

4.2 Data Integration

Experience combining raw data from multiple sources within and across different domains.

Preferred Experience: 5 years

4.3 Business Problem Analysis

Experience understanding complex business questions and formulating appropriate analytical questions to solve business problems.

Preferred Experience: 5 years

4.4 Data Visualization and Analytical Modelling

Experience preparing data visualizations, dashboards, and analytical models.

Preferred Experience: 5 years

4.5 Statistical Analysis and Data Mining

Experience with and understanding of statistical and data mining techniques used to address key business issues.

Preferred Experience: 6 years

4.6 Large-Scale Government Data

Experience working with large government datasets.

Preferred Experience: 4 years

5. Key Skills and Competencies

The ideal candidate should demonstrate the following capabilities:

• Strong programming and statistical analysis skills using Python, R, SQL, or similar technologies.
• Practical experience developing, training, evaluating, and implementing machine learning models.
• Expertise in data preparation, cleaning, normalization, and transformation.
• Knowledge of predictive and prescriptive modelling techniques.
• Experience with clustering, classification, regression, and other statistical methods.
• Ability to analyze large and complex datasets and generate actionable insights.
• Understanding of data governance, data quality, privacy, de-identification, and data ethics.
• Experience developing analytical products, reports, dashboards, and visualizations.
• Strong problem-solving, analytical thinking, and requirements-gathering skills.
• Ability to collaborate with cross-functional teams, data engineers, analysts, and business stakeholders.
• Effective communication, documentation, and knowledge-sharing skills.