Applied Scientist

📍 Location: Montreal, Canada

🕘 Employment: Full-time

🏢 Work model: On-site, 5 days a week

💰 Salary: CA$100,000 – CA$140,000 per year

🛂 Work authorisation: Applicants must already have the right to work in Canada. Visa sponsorship is not available.

We're hiring for a health-tech Series A startup that lets people track their hormones continuously instead of relying on occasional lab tests.

You'll join the ML team as an Applied Scientist, reporting to the ML lead who built Eli's current system. The work is scientific problem solving on imperfect, noisy real-world data, not ML infrastructure; the ML lead supports integration and productionization.

Two data sources: the app's analysis of each test result (the color read by the ML engine), and the daily series of results that builds each user's curve.

You will own:

• Explore datasets and understand the underlying data-generating processes.
• Develop, evaluate and improve machine learning, signal processing and statistical models.
• Run feature engineering, model selection, validation and error analysis.
• Identify confounding, data leakage, measurement variability and distribution shift.
• Design experiments and analyses to resolve uncertainty and guide modelling decisions.
• Investigate new approaches, from classical ML to deep learning where appropriate.
• Write clear, reproducible Python code (beyond notebooks) and communicate findings to technical and non-technical stakeholders.
• Build analyses and models with production deployment in mind.
• Investigate model performance and failures using real production data.
• Run ongoing error analysis, diagnostics and root-cause investigations.
• Spot distribution shifts, edge cases, systematic biases and performance degradation.
• Turn production observations into experiments, improvements or data-collection strategies

What we're looking for ?

• Bachelor’s degree in Engineering, Computer Science, Data Science, Mathematics or a related field. Master’s or PhD preferred.
• Minimum 5 years of professional experience, excluding internships.
• Strong foundation in machine learning fundamentals, statistics and experimental reasoning.
• Strong Python and experience with common ML and data science libraries.
• Able to work independently on ambiguous problems and decide which questions need answering.
• Good grasp of model validation, uncertainty, bias/variance and generalisation.
• Able to tell a statistically or scientifically meaningful improvement from one that only moves a metric.

Why join?

• Be part of the core team developing and commercialising the first product to monitor hormonal data daily and over a lifetime.
• Join at an early stage, in a small (under 25), high-performing and diverse team where your work shapes the company’s direction.
• Autonomy: people drive their own work and solve open-ended problems proactively.
• Health insurance (medical, dental, vision and more), plus flexibility over schedule and vacations.
• Office and R&D facilities in Montreal, with a distributed team that favours asynchronous work and fewer meetings