Data Scientist – Medical AI

Data Scientist – Medical AI (Computer Vision & Physiological Signals)

About the role

Caare Healthtech Services is building clinical-grade AI screening modules: contactless cardiovascular monitoring from video (rPPG), oral cavity image analysis, and dermatological lesion classification. You'll own models end to end, from data and training through evaluation and deployment-ready inference APIs, in a small team with hard milestone deadlines. Beyond these modules, you'll also contribute to new AI projects as Caare's product portfolio grows.

What you'll do

• Train, fine-tune, and benchmark deep learning models for image classification, detection, and segmentation (oral lesions, skin lesions) and for video-based signal extraction (HR, HRV, SpO₂, BP estimation, stress indices)
• Build preprocessing pipelines covering image quality gating, ROI detection, motion artifact and lighting correction, and signal denoising
• Design rigorous evaluation: sensitivity/specificity, calibration, confidence scoring, and subgroup performance across skin tones, age, and demographics, with honest reporting of limitations
• Survey and reproduce recent research, and decide what's viable for production versus what's still a research claim
• Package models as low-latency inference services (cloud and edge) with standardised outputs, audit logging, and secure APIs
• Write technical documentation covering architecture, training methodology, validation results, and integration
• Support clinical validation activities with technical evidence and fast iteration on feedback

Must have

• 2+ years in applied ML/data science, with models you personally trained and deployed
• Strong PyTorch skills, with hands-on work in CNNs/ViTs, transfer learning, fine-tuning foundation models, and data augmentation for small or imbalanced datasets
• Solid evaluation discipline: proper train/val/test splits, leakage prevention, statistical testing, calibration, and error analysis
• Experience with medical imaging, biosignals, or other high-stakes domains
• Ability to read papers critically and translate them into working code
• Deployment experience with ONNX/TensorRT or similar, FastAPI, Docker, and at least one cloud platform

Strong plus

• rPPG, PPG, or physiological signal processing (filtering, spectral methods, HRV analysis)
• Experience with dermatology or oral pathology datasets (ISIC, HAM10000, or similar)
• Fairness evaluation across skin types (Fitzpatrick scale)
• Familiarity with GDPR, medical device software standards (IEC 62304, EU MDR), or clinical validation protocols
• Publications or open-source work in medical AI
• Edge/mobile model optimisation (quantisation, pruning)

What we offer

• Ownership of core models in a company that retains its technology
• Competitive compensation
• Flexible/remote working

To apply

Send your CV to contact@caare.in and one example of a model you trained and evaluated, covering what metrics you used and what failure modes you found.