Are you looking to work in the cutting edge area of applying data-science to help global customers get a better insight into their health? Role Name: Senior Data Scientist
Science Team | Full-Time | In-Office | Bangalore (Kudlu Gate)
The Ultrahuman Science Team builds the algorithms behind the Ring, M1 CGM, blood and urine biomarkers, and Performance Lab assessments. We are hiring a Senior Data Scientist to own those algorithms end to end: from the raw sensor signal to a model that is shipped, monitored, and trusted in users' hands.
In a typical month you will improve a production algorithm, root-cause a metric users are complaining about, and stand up the data pipeline the next model needs. Ship models, not notebooks: you prove a change on our own cohort before it reaches users, and a model is done only when it runs in production and you can tell how it is behaving
Validate against reference standards: design evaluations against gold standards, reference devices, and study ground truth, and know when a result is real and when it is an artifact
Own the data layer: cohort extraction, feature pipelines, study data, and raw sensor data, so the next model starts from clean inputs

Improving production algorithms - Take an existing production model like sleep staging, root-cause the failure modes against reference data, and ship a fix you can defend with numbers.
Building new models - Train an activity classifier on raw sensor data, design the labeled data collection that expands it, and pick the operating point so false positives never erode trust.
Proving it before it ships - Run a new steps algorithm against reference-device cohorts, decide with data when it is ready, and monitor how it behaves after rollout.

High ownership, end to end: you take a problem from a vague question to a shipped model without waiting to be handed scope,



and you can point to something you owned from raw data all the way to production
You've worked with human health data: wearables, physiological signals, or clinical data.

If your experience is close but not exact, show us why you will ramp rapid
You've built at a startup: or somewhere small enough that nobody handed you clean data, clear specs, or a mature ML platform
You work like it's 2026: coding agents and AI tooling are part of how you build every day, and you can tell which new capabilities are worth adopting
Languages and data: Python and SQL daily, comfortable working in a real codebase
Machine learning: Advanced machine learning: time series and sequence models, deep learning on continuous physiological signals, and ensembles
Statistics and evaluation: hypothesis testing, experiment and A/B design, model evaluation, and error analysis against a reference standard
Scale and cloud: Spark or equivalent on large datasets, and AWS, GCP, or Azure
Production ML and MLOps: training pipelines, model versioning, deployment, monitoring, and drift detection
LLMs and agentic systems: fine-tuning and serving models, building agentic pipelines, and using coding agents to move faster

Experience:
- 4 to 5 years building and shipping machine learning systems. We index on what you have shipped and on trajectory, not the exact number of years; Bachelor's or higher in engineering, computer science, statistics, or a related field.

The Science team is small and moves fast, and much of the work has no precedent to copy.
- If you need a mature data org, clean labelled datasets, and clear guardrails to thrive, this particular role will not be the right fit, and that is worth knowing up front.

Ownership of algorithms that hundreds of thousands of people see every morning
100M+ nights of sleep and continuous physiological signals at scale
Direct collaboration with the engineering, product, and design teams building Ultrahuman

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