Our main client groups are big pharma companies, biotechs and medtechs. We also work for other companies in the healthcare space such as service providers and life science VCs. See the logos on our website for examples. Almost all our client relationships are multi-year partnerships.

Yes, we also develop software for our clients. 

Depending on the nature of the application and the context, we may either focus on the “AI core” (backend) or choose to develop the entire application ourselves (backend and frontend).

Broadly speaking, the goal of strategy work is to advise our clients on what to do in specific situations, often with high uncertainty. As such, the topics can vary widely, for instance:

• Validating business models for AI opportunities

• Developing technical realization strategies in the context of complex legacy technology landscape

• Figuring out how to best position and communicate AI and data assets in preparation for an IPO 

• Due diligence of AI technology and teams

• Organizational transformation of existing data functions towards AI product teams

• Comprehensive AI strategy development 

In terms of overall time spent, the split is roughly 30% strategy and 70% solutions. Most of our projects have both strategy and solutions work streams.

We focus on developing solutions addressing the needs of our clients. To do so, existing AI algorithms are some of the building blocks, and we rarely develop novel methodology.

However, our work almost always involves a lot of creative sophisticated quantitative modelling “around” standard algorithms, to address the actual problem at hand. This is an area that is largely out of focus within the AI research community, but no less intellectually challenging. 

For example, imagine a scenario where you want to filter out irrelevant alarms in an intensive care unit based on real-time analysis of a patient's vital sign data. In a typical machine learning approach, this task might be reduced down to a binary classification problem (relevant vs irrelevant alarms), which ends with an AUC that seems “high”. But this is just the end of the beginning. How exactly does that classifier output translate into actions? Options include suppressing, prioritizing or delaying alarms – and possibly more. And how exactly does that result in workflow efficiency, better care, or cost reduction? 

That entire process needs to be modelled quantitatively. How else can you decide, for example, whether it makes sense to invest time in increasing the AUC by 0.05? Which mode of intervention is the best? How to balance the different types of impact? Or for which patient segments, times of day, staffing cycles we need better data? 

The typical number of people working on a single project range from two to four. Through the way we set up projects, we ensure that there’s always enough senior supervision for guidance and learning on the job.

Our organization and career path follows the classic apprenticeship-to-partnership model common among architecture and law firms, as well as strategy consulting and venture capital. 

This means that, without exception, everyone starts with hands-on client work, then learns to manage complex projects, before ultimately developing their own speciality and client segments. This you can think of as running your own business as part of idalab.

That said, while our general model is classic, we do things differently. You don’t need to worry about the typical stereotypes around law firms and strategy consulting.

What makes us special is that everyone, without exception, has an unusually broad generalist skill set, covering engineering, AI methodology, communication, strategy and concept and healthcare industry know-how.

To achieve this, in the first years, everyone goes through the same basic training, before joining either the strategy or the solution discipline. 

Most of our projects have both strategy and solutions work streams, collaborating closely with each other.

In our apprenticeship-to-partnership model, individual development isn’t just encouraged – it’s essential. We grow as a company only as much as our people grow.

Therefore, we have clearly defined career steps, a granular competence framework, regular feedback and training to support your development.

As career progression is very important for us, we have developed a lot of training in-house. This includes a wide range of topics:

• AI methodology & statistics

• Professional software engineering & architecture

• Leadership, presence and impact

• Conceptual and strategic thinking

• Communications (visual, written, oral)

• Regulatory compliance

• Life science & healthcare business models

• Key data sources & analysis

We mostly work from our office, but travel to our clients for workshops and important meetings.

We work predominantly from our office in Berlin. This is the best fit for the type of work we do and the nature of our organization. That said, there is some flexibility but we are not embracing home office.

We do a lot of great fun stuff, but don’t talk about it. Otherwise we’d need to make it less fun. Sorry.

We are an independent profitable team-owned company without outside investors. 

We love making connections at the intersection of AI and life science. We host a regular event series with invited speakers (idalab Seminar), attend conferences, collaborate with the Berlin chapter of Nucleate, teach at university, sometimes even publish academic papers, and, every Christmas, collect money for the Schutzengelwerk.

What we’re not doing (yet) is contributing to open source projects, but may well do in the future.

Yes, we have a dedicated Student Trainee role. They get client exposure, structured career development, advanced training and the ability to grow their network.

You cannot apply directly though, the first step is to do an internship with us.

The first step is to get to know us through our annual internship program.

As in virtually all high-end professional services firms, there is no separate sales function. Instead, the responsibility for business development increases with seniority. 

From early on, you will have full transparency into our business development activities and start to contribute to proposals already in your first years. Later on, we support your own initiative in developing your “sweet spot”.

In principle, everyone in our team is client-facing, without exception, to ensure we all truly understand the challenge to be solved and its entire context. Sometimes we even fully embed ourselves within client teams. 

That said, in our project setup we have clearly defined roles, so not everyone is doing everything all the time, and to make space for focused deep work. We also spend a lot of time practicing clear and convincing communication, both on the job and in dedicated training sessions.