Resilience Care is a leading French remote patient monitoring (RPM) company and a clinical research partner, founded in 2021 with a mission: better care.
We provide RPM solutions in oncology, gastroenterology and psychiatry, combining ePRO collection with AI techniques. Our platform supports healthcare professionals with early side‑effect detection for continuous and proactive care, and our patient mobile app helps measure and manage symptoms with tailored resources.
Our solutions improve care pathways, enrich continuous patient understanding, and accelerate clinical research through the collection, structuring and in‑depth analysis of real‑world data. They are currently deployed in routine care for 35,000 patients across 200+ healthcare institutions, and also support around twenty academic and industry clinical studies.
We put data at the service of care and therapeutic innovation, with the ambition to enable every patient to benefit from personalized medicine.
In a nutshell: You will design and deliver machine learning models on real-world patient health data to support Medical Affairs studies and build emerging product capabilities for the future devellopement of the Resilience product.
Your impact: Bring strong ML methodology, reliable models and actionable outputs to Medical Affairs and Product teams, in a scale-up environment focused on delivery.
Your day-to-day
Build predictive models for Medical Affairs studies on patient datasets.
Work with longitudinal/repeated-measures data (time series, repeated observations) using appropriate approaches.
Define validation strategies and metrics aligned with clinical / real-world constraints.
Deliver “light” productionization: automated recalculation, scheduling, scoring, and monitoring.
Partner with Medical and Product to frame needs and communicate methodological choices clearly.
Contribute to applied healthcare ML watch and propose practical improvements.
You are a good fit if you can:
Code in Python and/or R.
Use SQL for data extraction and preparation.
Apply ML methods to healthcare datasets with real-world constraints.
Handle longitudinal / repeated-measures data (e.g., mixed-effects models).
Set up scoring/recalculation pipelines and basic monitoring.
Explain assumptions, limitations and results in a clear, inclusive way.
You are a good fit if you have:
Strong ownership and autonomy.
Humility and a learning mindset.
Tenacity and problem-solving skills.
Ability to manage several topics in parallel.
Flexibility in a scale-up context.
Collaborative communication with cross-functional teams.
You are a good fit if you have already:
3–5 years of Data Science experience (or equivalent).
Delivered models to production (even lightweight) or shipped scoring pipelines.
Worked with non-technical stakeholders to scope and deliver outcomes.
Exposure to healthcare / biostat / life sciences (highly preferred).
Nice to have:
A clear mission: improving patient care with data.
High-impact topics: real-world data, clinical research acceleration, emerging product capabilities.
A results-oriented scale-up environment with strong expectations on quality.
Screening (15 min): motivation, prerequisites and context.
Manager interview (45 min): role expectations, methods and collaboration.
Case study (45 min to 1 h): technical discussions and practical use cases with multiple interviewers.
Team fit - Panel interviews (30 min each): discussions with multiple interviewers of the team.
GDPR : Your personal data will be processed for the purposes of recruitment related activities, which include setting up and conducting interviews and tests for applicants, evaluating and assessing the results thereto, and as is otherwise needed in the recruitment and hiring processes. They will be available only for people involved in the process and erased after 2 years of inactivity.
Under GDPR and as Resilience attach great importance to privacy, please note that you have the right to request access to your personal data, to request that your personal data be rectified or erased. The Data Protection Officer can be contacted at [email protected]
For more information, please check our privacy policy.
