Machine Learning Specialist

Job not on LinkedIn

🕒 August 12

🇨🇦 Canada – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 10%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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PATH

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 1977

💰 Grant - PATH on 2025-07

PATH is a global nonprofit dedicated to health equity. With nearly 50 years of experience forging multisector partnerships, and with expertise in science, economics, technology, advocacy, and dozens of other specialties, PATH develops and scales up innovative solutions to the world’s most pressing health challenges.

📋 Description

• Provide hands-on support, leadership, advice, and direction on strategic data initiatives • Coordinate with internal and external clients to understand analytics needs and determine how data can best meet them • Create tools, models, and analyses applying artificial intelligence to improve government policy and services • Work with cross-functional project teams to apply machine learning • Help teams identify when and when not to apply machine learning, including prerequisites for effective applications • Facilitate, coach, and mentor others in applying machine learning to complex public challenges • Help identify and select machine-learning tools, services, and infrastructure • Create data collection, normalization, and cleaning procedures • Create training scripts and train models for specific domains using selected machine-learning packages • Run machine-learning-driven analyses of large datasets and report findings • Create ML analytics, reports, and insights to inform better services and policymaking • Integrate trained ML models within applications • Develop auditing, accountability, and transparency mechanisms for ML capabilities • Work within privacy legislation and provide ethical and practical guidance on ML implementation • Develop and share analytical models and products • Analyze and organize raw data for prescriptive and predictive modeling and build algorithms delivering business value • Support development of full-stack data analytics or AI applications as required • Provide expertise and leadership in the design and completion of analytic projects • Conduct complex data analysis and collaborate with data engineers and analysts • 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 • Perform other responsibilities as required or requested

🎯 Requirements

• Expertise in machine learning and a diverse range of analytical skills • Experience in machine-learning model development, artificial intelligence, data analysis, data science, AI development, data engineering, data modelling, or statistical analysis • Knowledge of data architecture, technical analysis, business analysis, and data product design and delivery • Ability to apply machine learning to complex public challenges • Knowledge of ML tools, services, infrastructure, and packages • Ability to create data collection, normalization, and cleaning procedures • Ability to create training scripts and train domain-specific models • Experience running ML-driven analyses of large datasets • Ability to integrate trained ML models within applications • Knowledge of auditing, accountability, and transparency mechanisms for ML capabilities • Ability to work within privacy legislation and provide ethical guidance on ML implementation • Skills in prescriptive and predictive modelling and algorithm development • Experience with full-stack data analytics or AI applications • Knowledge of statistical classification techniques including k-means clustering, hierarchical clustering, partition trees, and logistic regression • Ability to gather and document client requirements • Ability to capture business and technical metadata for ML products • Ability to work in a multi-vendor/staff environment