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Data science & machine learning

Algorithms from notebook to production. Predictive models integrated into clinical workflows, MLOps infrastructure that keeps models performing, and documentation your team can maintain.

We build machine learning systems that ship.

Data Science & Machine Learning at Toboggan means taking algorithms from notebook to production deployment. We design predictive models that integrate with existing clinical workflows, build ETL pipelines that handle the chaos of real-world healthcare data, and create monitoring systems that catch model drift before it becomes a patient safety issue.

The outcome: production ML systems that clinical teams actually use, with the infrastructure and documentation your team needs to maintain them.

How we work

What we believe

1

Healthcare ML requires different engineering cultures.

You can't A/B test your way to clinical algorithms the way you might A/B test ads. Clinical validation takes time, regulatory review takes longer, and model performance matters in ways that ad click-through rates don't.
2

Transparency over accuracy points.

A model that's 2% less accurate but clinically interpretable will get adopted. A black box that performs slightly better will sit unused because clinicians don't trust what they can't explain. We optimize for trust and adoption.
3

Model maintenance, not just model training.

The hard part isn't building the first version—it's the system that retrains on new data, detects when performance degrades, and makes it easy for your team to iterate without us.

FAQs

Yes—that's most of our work. We've built ETL pipelines that pull from decades-old EMR databases, handle HL7 v2 messages, and reconcile patient records across systems that weren't designed to talk to each other. We're comfortable in the mess.

Ready to build?

Let’s talk. No matter what stage you’re at, we’re happy to discuss your project.
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