Senior Data Scientist

InterviewPanel; Bengaluru; On-site; INR 2,500,000 - 4,500,000; Full time

Job summary

Ultrahuman is seeking a Senior Data Scientist to own algorithms end-to-end—from raw sensor data to production-grade models that run in users' hands. You will build and ship models, validate against gold standards, and own data pipelines, features, and study data.

The role emphasizes ownership, fast iteration, and working with time-series physiological signals, aiming to deliver trustworthy insights at scale in a health-focused product.

Qualifications

• 4–5 years building and shipping machine learning systems.
• Bachelor's or higher in engineering, computer science, statistics, or related field.

Responsibilities

• Own end-to-end ML algorithms from data to production model.
• Ship models and validate in production against metrics.
• Own data pipelines, feature extraction, and raw sensor data flows.

Skills

Python

SQL

PyTorch

TensorFlow

scikit-learn

Time series

Deep learning

MLOps

Production ML

Data analysis

Education

Bachelor's or higher in engineering/computer science/statistics

Tools

Spark

AWS/GCP/Azure

ML pipelines

Job description

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? If so, read on and apply.

Role Name: Senior Data Scientist

Science Team | Full-Time | In-Office | Bangalore

The Role

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.

This is a build role with real scope. 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. The common thread is ownership: you take a vague question and return a working answer, without waiting to be handed scope.

What You'll Do

• Own algorithms end to end: sleep staging, activity detection, sensor-derived metrics, and health scores. You frame the problem, build the features, train and evaluate the model, and see it live
• 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

What This Looks Like in Practice

1. 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.

2. 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.

3. 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.

Who You Are

The two things we can't coach

• 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
• Hungry for more scope: you have outgrown your current role and want problems biggerthan your title, with the technical depth to be trusted with them

Also important

• 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 fast

• 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
• You communicate: you can explain a model and its limits to a product manager, an engineer, or a founder, and hold your own with our scientists Core Technical Skills
• Languages and data: Python and SQL daily, comfortable working in a real codebase
• Machine learning: PyTorch or TensorFlow, scikit-learn, and gradient boosting, with the judgment to know which the problem needs
• 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; if you are a little earlier but have clearly outgrown your current scope, we want to hear from you.

- Bachelor's or higher in engineering, computer science, statistics, or a related field.

How We Work and Who Thrives Here

- The Science team is small and moves fast, and much of the work has no precedent to copy.

- People do their best work here when they are energized by ambiguity, low on ego, quick to adopt a better idea no matter where it comes from, and comfortable owning something before anyone has told them how. 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.

What You'll Gain

• Ownership of algorithms that hundreds of thousands of people see every morning
• A dataset most scientists never get to touch: 100M+ nights of sleep and continuous physiological signals at scale
• Direct collaboration with the engineering, product, and design teams building Ultrahuman