We have too much information in the world. Collecting data and extracting actionable insights is a waste of time. We should all just go back to being hunter gatherers. Put all of our hard drives on a space ship 🚀 and send it out to deep space — never to be seen again.

Did you just disagree with everything I wrote in the previous paragraph? Fret not, you might just be our new colleague!

Hold on a minute — what do you guys actually do that you have so much data? 🤔

Good question — thank you for asking! At Teton, we’re re-imagining the role of healthcare professionals, by building a collective AI-brain that can keep taps on every patient as if the staff was there them selves. Enabled by computer-vision devices in every room, we capture before unseen data that has the power to change the way care workers do their job.

Our company takes a vastly different approach to this otherwise highly regulated sector by adopting a silicon mindset and putting our users and the product in the focus. We’re like the 5’5” NBA players with lightning speed and exceptional maneuverability. We are not burdened by bureaucracy and we praise out-of-the-box thinking and take ideas to production in no time.

As I said, we listen to our users and always course correct accordingly. To that end, we collect large amounts of analytics data, intermediate states, model predictions, usage statistics, and more on a daily basis.

Sounds interesting — can I become a part of this?

Of course! We are looking for a talented person who’s intrigued by taking a bunch of data points, putting them in a juicer, and extracting the essence of underlying insights. You will be an integral part of shaping the future of our product. We truly believe in building our product based on a strong feedback loop comprising of data from both our software stack and our users. Your insights and suggestions will be of utmost value.

I am in 🤓 — what’ll the job be exactly?

You will mainly be working on analyzing our analytics data that we collect continuously. You will work closely with the engineering team by highlighting problem areas. In fact, you are encouraged to provide insights that I cannot possibly list here since we are not currently aware of them.

Additionally, you will work on monitoring and reviewing the performance of our software stack — mainly the embedded part. This work involves evaluating the accuracy of our models in production as well as reviewing the generated events (which are our outputs towards the users). For example, we’re providing users with the ability to set alerts when a patient undertakes a specific action. It will be your responsibility to take tabs on the accuracy of our outputs and guiding the product in a direction that improves such metrics.

Together, we’ll also formulate a set of KPIs and related metrics that we can use to follow the progress of our product. This is something that we only have partially and informally, so your contribution in this regards will be essential.

You will also directly interact with our AI team and be part of data related processes. Not only do we collect analytics, we also collect data that is used to train our models. You will be part of scripting and maintaining data sourcing tools.

We also appreciate and encourage one’s working outside their expert areas. So you will have the opportunity to also put your own spin on how and what you work on as a member of the team.

(We value fast learners more than experience with specific technologies. If you’re missing a few of the following requirements, but you’re up to the task, please apply)

We expect you to:

• Have experience with processing and analyzing data.

• Be curious and driven to help shape the product for the better by providing actionable insights.

• Be ready to get your hands dirty and push the limits of the healthcare industry.

• Have experience with Python.

• Know how to construct SQL queries.

• Have good communication skills so that you can present your findings in a clear and concise way.

• Be fluent in English.

• Be independent and a good team player.

• Understand the importance of data, data collection, and data analytics in the context of deep learning and AI.

• Reside in (or near) Copenhagen so that you’re able to be present in the office.

• Be legally allowed to work in Denmark.