The opportunity

This role combines applied data science, clinical analytics and research.

You will help develop the analytical and machine-learning capabilities that allow Braive to learn systematically from every completed treatment. Your work will support our ambition to provide increasingly precise, adaptive and continuous care: identifying what works, for whom, under which circumstances and at what point in the treatment journey.

You will work across the full data-science lifecycle, from establishing reliable analytical datasets and defining clinically meaningful outcomes to developing, evaluating and operationalising statistical and machine-learning models.

A central part of the position is the opportunity to complete an Industrial PhD while employed by Braive. We expect the doctoral project to be undertaken in collaboration with a degree-conferring university, most likely KTH Royal Institute of Technology, while continuing our established research relationship with leading research institutions including the University of Oslo.

The doctoral component is subject to formal university admission, agreement with the academic partner and funding through the Research Council of Norway’s Industrial PhD Scheme.

The research direction

The precise doctoral research question will be developed jointly by yourself, Braive and the academic supervisors. It will address a scientifically significant problem that is also directly relevant to Braive’s long-term clinical and technological development.

Potential research areas include:

• Longitudinal modelling of symptoms, engagement, treatment activities and clinical outcomes

• Identification of clinically meaningful treatment trajectories and early indicators of improvement, deterioration or disengagement

• Estimation of heterogeneous treatment effects to understand which interventions work best for different patients

• Development of adaptive treatment policies and personalised recommendations

• Sequential decision-making and dynamic prediction during treatment

• Causal evaluation of treatment components and changes to digital clinical workflows

• Natural-language processing and large language models for analysing clinical documentation and therapeutic processes

• Reliable evaluation, calibration, monitoring, and governance of machine-learning models used in clinical settings

• Methods for converting routinely collected clinical data into validated and actionable decision support

The research must be methodologically rigorous, clinically relevant, ethically responsible and suitable for publication in peer-reviewed scientific venues.

What you’ll do

Develop applied data science for precision-based care

You will:

• Analyse longitudinal clinical and behavioural data to understand treatment processes and outcomes

• Develop statistical and machine-learning models that support personalised assessment, treatment planning, monitoring and follow-up

• Define appropriate target variables, cohorts, evaluation frameworks and clinical success criteria

• Apply methods such as longitudinal analysis, hierarchical modelling, survival analysis, causal inference, predictive modelling and treatment-effect estimation where appropriate

• Evaluate models for discrimination, calibration, robustness, generalisability, fairness and clinical usefulness

• Translate analytical findings into product improvements, clinical insights and testable research questions

• Design prospective and retrospective evaluations of new treatment functionality

• Establish processes through which each completed treatment contributes to better future care

Build a reliable analytical foundation

Advanced modelling depends on reliable data. You will therefore retain significant responsibility for Braive’s analytics platform.

You will:

• Develop and maintain well-structured analytical datasets for clinical, product, operational and commercial use

• Own and improve our dbt project, including staging, intermediate and mart layers

• Design dimensional and longitudinal data models that support both business intelligence and scientific analysis

• Establish consistent definitions for clinical outcomes, engagement, treatment exposure, operational performance and commercial reporting

• Implement automated data-quality tests and monitoring

• Improve the performance, maintainability, lineage and documentation of analytical models

• Ensure that reported metrics can be reproduced and defended during clinical review, customer evaluation and regulatory audit

• Support trusted self-service analytics through ThoughtSpot and related tools

Operationalise research and models

Your work should move beyond exploratory analysis.

You will:

• Work with product and engineering teams to integrate validated models and analytical methods into production workflows

• Develop reproducible pipelines for feature generation, training, validation, deployment and monitoring

• Define safeguards and human oversight for data-driven clinical decision support

• Monitor changes in data quality, model performance, patient populations and clinical practice

• Document model purpose, assumptions, limitations, validation evidence and intended use

• Contribute to technical and clinical documentation required within a regulated medical-device environment

• Help distinguish between exploratory research, internal decision support and functionality requiring formal clinical or regulatory validation

Collaborate across clinical, academic, and commercial teams

You will:

• Work closely with psychologists, researchers, product managers, engineers, commercial colleagues and operational teams

• Translate clinical and business questions into clearly defined analytical or scientific problems

• Communicate results in a way that is accurate and useful to both technical and non-technical audiences

• Collaborate with academic supervisors and research groups at KTH and potentially UiO

• Contribute to research protocols, ethics and data-protection assessments, scientific manuscripts, conference submissions and funding applications

• Maintain a clear connection between the doctoral research and Braive’s clinical and product strategy

• Help strengthen Braive’s internal competence in applied statistics, machine learning, and responsible clinical AI

What success looks like

During your first six months

You will have:

• Developed a detailed understanding of Braive’s clinical data, treatment pathways, analytical architecture and regulatory context

• Improved the reliability, structure and documentation of priority analytical datasets

• Established agreed definitions for core clinical and treatment-process measures

• Completed at least one substantive analysis that informs a clinical, product, or commercial decision

• Defined an initial doctoral research direction with Braive and the prospective academic supervisors

• Contributed to the Industrial PhD application and university-admission process

• Produced a practical roadmap connecting the research programme with Braive’s product and data strategy

• Continue to develop Braive’s post-market surveillance procedures

Within the first year

You will have:

• Established a reproducible framework for analysing treatment trajectories and outcomes

• Developed and evaluated at least one advanced statistical or machine-learning approach using Braive’s clinical data

• Enabled clinical and product teams to use more reliable outcome and process measures in their work

• Improved the company’s ability to evaluate whether product and treatment changes produce meaningful benefits

• Begun translating research findings into validated analytical tools, product capabilities or clinical decision support

• Established a productive working relationship with the academic research environment

• Prepared research suitable for publication or presentation

Over time

The role will help make Braive’s data and research capabilities a meaningful clinical and strategic advantage.

Braive should be able to:

• Learn systematically from accumulated treatment experience

• Detect meaningful variation in treatment response

• Personalise treatment while maintaining clinical oversight

• Evaluate new functionality using scientifically defensible methods

• Provide clinicians with timely, relevant and interpretable information

• Demonstrate outcomes transparently to healthcare providers, insurers, regulators and research partners

• Advance the scientific basis for precise, adaptive, and continuous mental healthcare

How the Industrial PhD component will work

The intended doctoral project will be developed jointly between yourself, Braive and the academic institution.

Subject to the final funding and collaboration structure:

• You will be employed by Braive throughout the doctoral project

• KTH is expected to be the primary degree-conferring institution

• UiO may contribute through research collaboration or co-supervision where appropriate

• Your time will be divided between Braive and the university in accordance with the Industrial PhD requirements

• Braive will appoint an internal mentor, and the university will appoint the required academic supervisor or supervisors

• The project will include doctoral coursework, research milestones, scientific publications, progress reviews and submission of a doctoral thesis

• The research will be designed to meet both university standards for scientific independence and Braive’s need for relevant, applicable knowledge

• Publication, intellectual-property rights, data access and the use of research outputs will be governed by formal agreements between the participating organisations

The likely structure is a four-year project in which at least 75% of your time is devoted to doctoral research and up to 25% is devoted to related responsibilities at Braive. The final structure will be agreed with the academic institution and set out in the funding application and employment arrangements.

We’re so excited that you’re thinking about joining us. If this sounds just as exciting to you, apply here. We can’t wait to meet you!