Senior Data Scientist
at Global Wearables Tech Lead with offices in US, EU, ME and IN
Data Science
Machine Learning (ML)
Python
Amazon Web Services (AWS)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
Shubham Vishwakarma
Full Stack Developer - Averlon
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JOB DESCRIPTION
Senior Product Manager (Technical)
Real-time AI Platform · Enterprise B2B · Multi-Vertical
Company: ableCredit
Location: Bangalore
Reports to: CEO / Co-founder
Team: Engineering, AI/ML, Enterprise Customer Success
Stage: Post-PMF, Pre-Scale
About ableCredit
ableCredit is a GenAI infrastructure company built for enterprises where human conversations drive high-stakes decisions. Our real-time AI co-pilot platform listens to live conversations, guides front-line agents through structured workflows, and captures everything into compliant, structured outputs — automatically.
Why This Role Exists -
This role exists to solve three specific problems:
- Shipping cadence: We build good things but ship unpredictably. We need someone who can install a culture of weekly releases, clear ownership, demo days, and “what shipped this week” accountability.
- AI leverage: We need someone who sees these opportunities instinctively: POC onboarding, SOP configuration, customer telemetry analysis.
- Alignment: The person in this role becomes the connective tissue between Business, engineering, and management - translating customer urgency into engineering priorities without losing either side’s trust.
What You’ll Own
Product Execution & Shipping Culture
- Own the product roadmap for the core ableCredit platform across BFSI, insurance, healthcare, and BPO verticals.
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- Own the prioritisation framework: what gets built, what gets deferred, and why. Communicate tradeoffs clearly to engineering, sales, and leadership.
Technical Product Leadership
- Work directly with the engineering and AI teams on architecture decisions, latency tradeoffs, model selection, and evaluation frameworks for AI features.
- Review telemetry dashboards, debug production issues alongside engineers, and read API documentation when integrating with client systems.
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AI as Internal Leverage
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- Stay current on AI capabilities (agentic frameworks, evaluation methods, prompt engineering at scale) and translate them into practical product decisions.
Enterprise Customer & Cross-Functional Alignment
- Be the single point of truth between what customers need, what sales has promised, and what engineering is building.
- Join customer calls and on-site visits. Understand the reality of a credit officer in Coimbatore or a branch executive in Nagpur using the product in the field.
- Build a visible, credible roadmap that sales can sell from and customers can plan around .
What We’re Looking For
Non-Negotiables
- 5–8+ years in product management, with at least 2 years shipping AI-powered or data-heavy products to enterprise B2B customers.
- Technical fluency: You’ve written production code at some point in your career. Today, you can read a Grafana dashboard and spot a latency spike, review a PR for product correctness, navigate API docs independently, and check telemetry without filing a ticket. Engineers see you as a technical peer, not a business translator.
- AI product judgment: You understand the difference between AI-as-product and AI-as-infrastructure. You’ve made decisions about model selection, prompt engineering, evaluation frameworks, and graceful degradation in production. Bonus: you’ve built or shipped agentic workflows.
- Shipping culture builder: You’ve joined a team that was building good things but shipping unpredictably, and you changed that. You can describe exactly what you did in the first 90 days.
Strong Preferences
- Multi-vertical or horizontal platform experience: You’ve worked on a product that serves multiple industries and have dealt with “build once, configure many” vs. “this vertical needs something custom” tensions.
- India enterprise context: Comfort with BFSI, regulated industries, or India-specific enterprise sales cycles. You understand that deploying in Tier-2/3 India is a different operating environment than a Valley SaaS playbook.
- Internal AI adoption: Evidence of using AI, automation, or agentic tools to improve your own team’s velocity — not just building AI products for customers.
Nice-to-Haves
- Experience with real-time systems: audio/video streaming, low-latency pipelines, multilingual NLP.
- Startup experience at a comparable stage (post-PMF, pre-scale, 20–80 people).
- Familiarity with Indian language tech, voice AI, or field-force enablement products.
Why Join ableCredit Now
- The AI is real. This isn’t a company that slapped “AI-powered” on a dashboard. Real-time transcription, multilingual voice models, agentic workflows - in production, at scale, in one of the hardest operating environments on earth.
- The expansion is genuinely exciting: four new verticals, a new global product, and 7x revenue growth targeted in a single year.
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High-dimensional feature stores—we work with hundreds of thousands of derived features. Experience managing, versioning, and serving large feature sets is valuable.
ENGINEERING STANDARDS
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Documentation: Schemas, data dictionaries, pipeline dependencies, operational runbooks. Others can understand and maintain your systems.
Testing: You write tests for pipelines—validation logic, transformation correctness, edge cases. Untested pipelines are broken pipelines waiting to happen.
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PostgreSQL, Python, workflow orchestration (flexible on tool), cloud infrastructure (GCP preferred but flexible), Git.
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EDUCATION
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TO APPLY
Include: (1) CV/resume, (2) Brief description of a data pipeline you built and maintained, (3) Links to relevant work if available, (4) Availability and timezone.
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Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon