Applied Scientist

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.
• 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