Machine Learning Scientist

Eli Health • Montreal, QC

2d • Onsite

About The Position

Eli Health is seeking an on-site Applied Machine Learning Scientist to join their ML team. This role focuses on scientific problem-solving and applied machine learning, working with imperfect, noisy, and complex datasets. The scientist will collaborate closely with the ML lead, who will support the integration and productionization of their work. The position involves both open-ended scientific problems, turning real-world data into robust analyses and models, and staying connected to how models perform in production, diagnosing failures, and driving improvements. The ideal candidate will be adept at understanding problems deeply, selecting appropriate methods, validating improvements, and communicating findings effectively to both technical and non-technical stakeholders.

Requirements

• Bachelor's degree (Master’s or PhD preferred) in Engineering, Computer Science, Data Science, Mathematics, or a related field.
• Minimum of 5 years of professional experience excluding internships.
• Strong foundation in machine learning fundamentals, statistics, and experimental reasoning.
• Strong Python skills and experience with common ML/data science libraries.
• Ability to work independently on ambiguous problems and determine what questions need to be answered.
• Good understanding of model validation, uncertainty, bias/variance, and generalization.
• Ability to distinguish between improvements that are statistically or scientifically meaningful and those that simply improve a metric.
• Ability to answer: Given the data we have and the problem we are trying to solve, what can we conclude with confidence, what remains uncertain, and what should we do next?

Responsibilities

• Explore datasets and understand the underlying data-generating processes.
• Develop, evaluate, and improve machine learning, signal processing, and statistical models.
• Perform feature engineering, model selection, validation, and error analysis.
• Identify issues such as confounding, data leakage, measurement variability, and distribution shift.
• Design experiments and analyses to resolve uncertainty and guide modelling decisions.
• Investigate new modelling approaches, including classical ML and deep learning (when appropriate).
• Produce clear, reproducible Python code that isn’t limited to notebooks, and communicate findings to technical and non-technical stakeholders.
• Develop analyses and models with the expectation that it will be deployed into production.
• Investigate model performance and failures using real-world production data.
• Perform ongoing error analysis, diagnostics, and root-cause investigations.
• Identify distribution shifts, edge cases, systematic biases, and degradation in model performance.
• Translate production observations into experiments, improvements, or data-collection strategies.

Benefits

• Health insurance (medical, dental, vision, and more)
• Flexibility over schedule and vacations
• Unlimited free access to the Bota Bota spa in Montreal for the employee and a +1

What This Job Offers

• Job Type: Full-time
• Career Level: Senior