Machine Learning Specialist

Compunnel, Inc.; Edmonton; On-site; CAD 120,000 - 180,000; Full time

Job summary

Compunnel, Inc. is seeking a Machine Learning Specialist to design and develop analytical data products, ML models, and AI-enabled services that support evidence-based policy and improved public services.

You will work with cross-functional teams and stakeholders to apply statistical analysis, data science, and AI while ensuring privacy, ethics, and data strategy alignment. Responsibilities include guiding use cases, preparing data for modeling, developing and validating models, and delivering

Qualifications

• Minimum 9 years of relevant experience.
• Minimum 6 years of experience using Python, R, and SQL for data analysis, data science, and machine learning.
• Minimum 6 years of experience building analytical and quantitative analysis models to support business, operational, policy, or service-delivery objectives.
• Minimum 6 years of experience preparing and transforming data for prescriptive and predictive modeling, including data cleaning, feature preparation, normalization, and quality assessment.
• Minimum 6 years of experience applying data analytics and data science methods, including complex statistical modeling and interpretation of analytical results.
• Minimum 6 years of experience applying artificial intelligence or machine learning to data science and analytics use cases, such as anomaly detection, predictive monitoring, classification, forecasting, data transformation, and automation.
• Demonstrated knowledge of statistical classification and machine learning techniques, including k-means clustering, hierarchical clustering, partition trees, logistic regression, and related supervised and unsupervised methods.
• Demonstrated experience gathering and documenting client requirements, framing analytical questions, developing analytical products, capturing technical and business metadata, and communicating complex findings to technical and non-technical audiences.

Responsibilities

• Advise stakeholders and project teams on when and how to apply machine learning, including identifying appropriate use cases, data prerequisites, risks, limitations, and expected business value.
• Analyze, organize, normalize, clean, and integrate raw data from multiple sources to prepare it for descriptive, predictive, and prescriptive modeling.
• Design, develop, train, validate, document, and refine machine learning and statistical models using appropriate algorithms, packages, tools, and programming languages.
• Conduct ML-driven analysis of large datasets and communicate findings through analytical models, reports, visualizations, dashboards, and actionable insights for services and policymaking.
• Integrate trained machine learning models and analytical capabilities into applications, data products, and full-stack analytics or AI solutions as required.
• Develop and maintain auditing, accountability, transparency, metadata, data quality, privacy, security, and ethical governance mechanisms for ML products and services.
• Provide technical leadership, coaching, mentoring, requirements analysis, stakeholder engagement, risk escalation, and delivery support within multi-disciplinary and multi-vendor project environments.

Skills

Python

R

SQL

Data analysis

Machine learning

Statistical modeling

Data transformation

Governance

Job description

Job Summary

The Machine Learning Specialist designs and develops analytical data products, machine learning models, and AI-enabled services that support evidence-based policy and improved public services. Working with cross-functional teams, external stakeholders, data engineers, analysts, and business leaders, this role applies statistical analysis, data science, artificial intelligence, data modeling, de-identification, and synthetic data techniques to complex challenges. The position provides hands-on technical leadership and strategic advice throughout the data product lifecycle while ensuring that ML solutions are reliable, explainable, privacy-compliant, ethical, and aligned with data strategy.

Key Responsibilities

• Advise stakeholders and project teams on when and how to apply machine learning, including identifying appropriate use cases, data prerequisites, risks, limitations, and expected business value.
• Analyze, organize, normalize, clean, and integrate raw data from multiple sources to prepare it for descriptive, predictive, and prescriptive modeling.
• Design, develop, train, validate, document, and refine machine learning and statistical models using appropriate algorithms, packages, tools, and programming languages.
• Conduct ML-driven analysis of large datasets and communicate findings through analytical models, reports, visualizations, dashboards, and actionable insights for services and policymaking.
• Integrate trained machine learning models and analytical capabilities into applications, data products, and full-stack analytics or AI solutions as required.
• Develop and maintain auditing, accountability, transparency, metadata, data quality, privacy, security, and ethical governance mechanisms for ML products and services.
• Provide technical leadership, coaching, mentoring, requirements analysis, stakeholder engagement, risk escalation, and delivery support within multi-disciplinary and multi-vendor project environments.

Required Qualifications

• Minimum 9 years of relevant experience.
• Minimum 6 years of experience using statistical and programming languages such as Python, R, and SQL for data analysis, data science, and machine learning.
• Minimum 6 years of experience building analytical and quantitative analysis models to support business, operational, policy, or service-delivery objectives.
• Minimum 6 years of experience preparing and transforming data for prescriptive and predictive modeling, including data cleaning, feature preparation, normalization, and quality assessment.
• Minimum 6 years of experience applying data analytics and data science methods, including complex statistical modeling and interpretation of analytical results.
• Minimum 6 years of experience applying artificial intelligence or machine learning to data science and analytics use cases, such as anomaly detection, predictive monitoring, classification, forecasting, data transformation, and automation.
• Demonstrated knowledge of statistical classification and machine learning techniques, including k-means clustering, hierarchical clustering, partition trees, logistic regression, and related supervised and unsupervised methods.
• Demonstrated experience gathering and documenting client requirements, framing analytical questions, developing analytical products, capturing technical and business metadata, and communicating complex findings to technical and non-technical audiences.

Preferred Qualifications

• Minimum 5 years of experience with data sharing, data linkage, de-identification, metadata, data quality, data ethics, synthetic data, data literacy, and the responsible use of data.
• Minimum 5 years of experience combining and analyzing raw data from diverse sources across multiple business or subject-matter domains.
• Experience preparing visualizations, dashboards, and analytical models; applying statistical and data-mining techniques to business issues; and working with large datasets, including a minimum of 6 years of statistical and data-mining experience and 4 years of experience with large datasets.