What hard skills we need to see 🛠️
Educational background: Engineering, computer science, applied mathematics, statistics, data science or equivalent experience.
Relevant experience: 7+ years of experience in data science, applied ML, ML engineering or data-heavy product engineering.
Python and SQL: Strong hands-on experience.
Data analysis: Strong ability to explore messy datasets, build cohorts, define labels, identify bias, produce clear visualizations and communicate what the data does and does not say.
Data engineering basics: Experience working with modern data warehouses and production datasets. BigQuery is a plus. dbt, Metabase or equivalent tools are a plus.
Applied ML: Solid experience with classical ML models, feature engineering, model evaluation, model monitoring and production constraints.
Production deployment: Ability to put a model into production yourself, at least through a simple backend, API, serverless deployment, batch pipeline or equivalent.
Product judgment: Ability to transform a vague ambition into a clear problem statement, a dataset, a baseline model, a metric and a decision framework.
Large-scale usage: Experience working on products, models or data systems used by a large number of users. Ideally hundreds of thousands of users or more.
What kind of person we are looking for 👤
Mindset: Pragmatic, structured and intellectually honest. You care more about solving the right problem than using the most impressive model.
You know when not to use ML: You are comfortable saying “this is not a ML problem yet”, “the data is not good enough”, “the label is too weak”, or “a rule-based system is better for now”.
You are pedagogical: You can explain complex technical trade-offs to non-technical leaders. You know how to align expectations without killing ambition.
You are end-to-end: You are not only a notebook person. You can go from raw data to analysis, from analysis to model, from model to deployment, and from deployment to monitoring.
You are product-minded: You care about user impact, not just model metrics. You understand that a churn model is only useful if it leads to actions that improve retention.
You are comfortable with ambiguity: You can take a vague idea like “AI should personalize the training journey” and turn it into a clear set of hypotheses, experiments, constraints and next steps.
You are a feminist: You believe women deserve better health solutions. You are comfortable working on topics such as pelvic floor health, childbirth, urinary leaks, female intimacy and women’s health in general.
Language proficiency: Fluent in English, with strong written communication skills. French is a plus but not mandatory.
You will shine if ✨
You have experience with IoT, connected devices, sensors or hardware-generated data.
You have worked on consumer apps, subscriptions, churn, retention or lifecycle personalization.
You have deployed models to large user bases and understand production constraints, monitoring, rollback, model drift and inference cost.
You have worked with mobile inference, embedded inference, ONNX, Core ML, TensorFlow Lite or similar tools.
You have experience building data products from scratch in a startup or scale-up environment.
You have experience with A/B testing, causal inference, quasi-causal analysis or experimentation frameworks.
You have worked in healthtech, digital health, medtech or another domain where model quality and user trust matter.
You have coached junior data scientists, analysts or engineers without necessarily being a people manager.
