Promptly Health

About The Company 
 
What’s Promptly in a nutshell 
 
Promptly is building the first patient-centered global evidence network, offering real world data sharing and monetization capabilities. Together with a selected network of Partners, we generate new knowledge from harmonized datasets, augmented with the collection of longitudinal patient-reported data and patient-generated digital biomarkers within a secure and privacy-preserving environment. 
 
We answer the question – is this patient treatment the best it could possibly be? 
 
What’s Our Purpose 
 
We exist to empower every patient and every health organization on the planet with evidence on the outcomes of care! 
 
Why do we get up in the morning? Well, most healthcare professionals have chosen to work in healthcare driven by their desire to make a difference in patients’ lives. And so have we! We have chosen to follow this calling by addressing the biggest problem in healthcare: the lack of real-world evidence on the outcomes of care. 
 
For us, society denying patients better care due to lack of access to data is unethical, in a world where technology improved so many aspects of our world. Making the right evidence available to healthcare organizations to help prevent one lost life, one care complication, one failed treatment is the moral obligation that big tech companies have – it’s our Hippocratic Oath. 
 
At Promptly, everything we do is driven by our core purpose: to promote better healthcare at lower costs for patients every day, by making health outcomes available. 
 
About The Role 
 
Promptly runs real-world evidence (RWE) studies for pharmaceutical sponsors on routinely collected hospital data: electronic health records, laboratory results and clinical documentation from a federated network of partner hospitals. Some of these data are harmonised to a common data model such as the Observational Medical Outcomes Partnership (OMOP) model; others arrive in the hospital's own format. Patient-level data never leave a hospital. Analysis code runs inside each hospital's trusted research environment (TRE), and only aggregate results leave after statistical disclosure control (small-number suppression). 
 
You are the statistician on these studies. You decide what a dataset can and cannot estimate, choose the design and the methods, quantify the uncertainty, and defend those choices to sponsors and clinicians. Everything we deliver has to stand up to methodological scrutiny, and you make sure it does. 
 
You do not work alone. Clinicians at the partner hospitals and medical experts are your closest collaborators. Together you define outcomes and exposures that are clinically meaningful and measurable in routine data, and interpret what the results mean for patients. A Clinical Data Engineer builds the data pipelines and the analysis datasets you specify. Depending on your background, you may also derive cohorts and variables yourself from prepared extracts. 
 
The team works in English. If you are early in your career, you will co-author your first protocols and statistical analysis plans (SAPs) with support from the RWE lead. You will grow into owning the statistical design and analysis of whole studies. If you are more experienced, you will own that from the start. The bar on statistical rigour is the same for both. 
 
What You'll Be Expected To Do 
 


* Turn clinical questions into study designs: estimand, target population and index date, comparator, confounding strategy, bias assessment, sample size and power.
* Work with clinicians and the sponsor's medical team to define outcomes, exposures and covariates. Check that what you measure in the data matches what they mean in practice.
* Specify the analysis dataset with the Clinical Data Engineer, or derive cohorts and variables yourself from prepared extracts in SQL and Python. Check data quality before analysis.
* Co-author protocols and SAPs with the RWE lead and clinical experts. The statistical parts are yours: estimands, analysis methods, sample size and how uncertainty is reported.
* Run the analysis in Python (or R): survival analysis (Kaplan-Meier, Cox, competing risks), propensity scores and multiple imputation, handling immortal-time bias and informative censoring. Derive inputs for health-economic models, such as transition probabilities.
* Prepare outputs that pass statistical disclosure control, then pool estimates across hospitals.
* Write the methods, statistical analysis and limitations sections of study reports, and present results to sponsors and clinical partners.
* Review sponsor protocols and concept sheets (short early study outlines), and say plainly what a proposed design can and cannot answer. 
 
 

What We're Looking For (qualifications & Skills) 
 
You do not need to match every point. If you have a strong statistical foundation and want to learn the rest, please apply. 
 

* An MSc or PhD in biostatistics, statistics, epidemiology or a related quantitative field, or equivalent applied experience.
* Strong foundations in statistical inference and observational study design: confounding, selection bias, and how to quantify and communicate uncertainty.
* Hands-on experience with time-to-event analysis, from a job, a thesis or a research project. Propensity scores, competing risks and multiple imputation are a plus, not a requirement.
* Python and SQL to a standard others can run, read and review. R is welcome but not required.
* Curiosity about the clinical side: you ask what a diagnosis code, a lab value or a visit means in practice before you model it. You enjoy working with clinicians.
* Written methods work you can point to: a protocol, an SAP, a paper or a thesis chapter.
* Clear English: you can explain a method and its limitations to a non-statistician, in writing and in meetings. 
 
 

Preferred Qualifications 
 


* Experience with hospital or claims data, including clinical coding systems such as the International Classification of Diseases (ICD-10) or SNOMED CT.
* Familiarity with the OMOP common data model and the Observational Health Data Sciences and Informatics (OHDSI) tools.
* Experience with federated or distributed analysis, or with work inside a TRE.
* Experience with health-economic modelling or health technology assessment (HTA) submissions.
* Experience of discussing methods with pharmaceutical sponsors, clinicians or other external partners. 
 
 

Position 
 


* Remote-first
* Full time 
 
 

What We Offer 
 
Financial Benefits 
 


* Competitive salary
* Annual performance bonus
* Equity compensation via our ESOP (open to all team members)
* Annual training allowance
* Home-office equipment allowance 
 
 

Non-Financial Benefits 
 


* Equal opportunity and inclusive environment
* Flexible work schedule and vacation policy
* Corporate events and international team gatherings 
 
 

What Is The Recruiting Process Like 
 


* There is a first interview with your future manager and/ or seasoned team members to get to know you and to give you an overview of Promptly and of the job you are applying to
* Afterwards, you will undergo a technical assessment stage, in which you will work on a test task or solve a business case.
* In same exceptional cases, a third interview might take place. Don’t worry if this happens. It’s because we have a good opinion on you, but there might be something we still need to get clear
* Finally, you will get your offer, and hopefully you’ll accept it!
* Also, we would like to notify you that during the final phases os the interview process, we may perform reference checks to validate the information provided in your application, resume, and discussed during the interview. 
 
 

Our Culture & Values 
 
Empathy — Ownership — Responsibility — Teamwork — Excellence 
 
If you embrace these values, you will thrive at Promptly. We are a purpose-driven company where everyone acts as an owner, committed to improving healthcare through data, technology, and collaboration. 
 
Privacy notice for applicants 
 
Promptly - Software Solutions for Health Measures, S.A. (“Promptly”) is the controller of the personal data contained in your application. We process that data to manage our recruitment activities: reviewing applications, organising and conducting interviews, tests and assessments, evaluating candidates, communicating with you throughout the process and, where an offer is made, preparing your hiring. We do so on the basis of the steps taken at your request prior to entering into a contract, and of our legitimate interest in selecting suitable candidates for the role. Your data is accessible only to the members of the Promptly team involved in the selection process and to service providers that support the tools we use to manage it, acting on our instructions and bound by confidentiality and data protection obligations. We do not use your data for purposes unrelated to recruitment, we do not share it with third parties for their own purposes, and we do not take decisions about you based solely on automated processing. 
 
If you are not selected, we keep your application for up to 12 months so that we may contact you about future openings that match your profile. You may ask us to delete it at any time, and we will do so. 
 
You have the right to access, rectify, erase, restrict and port your personal data, and to object to its processing. To exercise these rights, or to ask any question about how we handle your data, write to dpo@promptlyhealth.com. You may also lodge a complaint with the Portuguese supervisory authority (Comissão Nacional de Proteção de Dados - CNPD). 
 
Applicant declaration 
 
By submitting this application, I confirm that the information provided by me, and in any documents attached to it, is true and complete to the best of my knowledge, and that I have not omitted any material fact. I confirm that I have disclosed any previous employment or engagement with Promptly. I accept that if any of the information provided is false or incorrect, my application may be rejected, any offer of employment may be withdrawn and, where employment has already begun, this may constitute grounds for disciplinary action, including termination, in accordance with applicable law.

Data

Analysis

Python

Network

Testing

Web Development

Security software

Data analytics

Research

Software Development

Support

Monetization

Project documentation

System security

Statistics

Data Pipelines

Team collaboration

SNOMED

Biostatistician / RWE Methodologist / Clinical Data Analyst