Role : Lead AI Architect & Principal Data Scientist (Healthcare & Life Sciences) :

We are looking for a visionary Data Scientist AI Architect to lead the design, development, and deployment of enterprise-grade AI/ML systems tailored for healthcare.

In this role, you will bridge the gap between advanced data science research and clinical-grade production systems, shaping our technical roadmap across diagnostics, predictive analytics, and clinical workflow optimization.

Key Responsibilities :

- Architecture & Strategy : Define the overarching technical blueprint for healthcare AI/ML platforms, ensuring high availability, scalability, and seamless integration with EHR/EMR systems (Epic, Cerner) and imaging networks (PACS/DICOM).

- Model Innovation & Development : Architect and guide the deployment of specialized ML, Deep Learning, and generative AI models (NLP/LLMs for clinical text, computer vision for medical imaging, risk-stratification models).

- Data Governance & Compliance : Embed regulatory and privacy frameworks into system architecture by design - enforcing strict compliance with HIPAA, HITECH, FDA guidelines for SaMD (Software as a Medical Device), and GDPR.

- MLOps & Pipeline Engineering : Lead the transition from proof-of-concept to robust CI/CD and MLOps pipelines handling multimodal health data, bias detection, drift monitoring, and explainable AI (XAI).

- Cross-Functional Leadership : Partner closely with clinicians, bioinformatics researchers, data engineers, and product leadership to translate complex medical requirements into technical solutions.

Qualifications & Experience :

- Healthcare Domain Expertise : 5+ years of direct experience working with healthcare data modalities (EHR, claims, clinical trials, or biomedical imaging) and standards (FHIR, HL7, DICOM, SNOMED CT, ICD-10).

Technical Mastery :

- Deep proficiency in Python, R, and frameworks like PyTorch, TensorFlow, and Hugging Face.

- Hands-on expertise architecting end-to-end distributed pipelines on AWS (HealthLake, SageMaker), GCP (Healthcare API, Vertex AI), or Azure Health Data Services.

- Strong foundation in statistical modeling, causal inference, and ethical AI/bias mitigation.

Systems Architecture : Demonstrated track record of deploying mission-critical machine learning systems into clinical or regulated environments.

Education : Master's or Ph.D. in Computer Science, Biomedical Informatics, Data Science, Electrical Engineering, or a related quantitative discipline (or equivalent practical experience).

Why Join Us? :

- Build technology that directly impacts patient care, clinician efficiency, and health equity.

- Lead a high-caliber team of researchers, clinicians, and software engineers.

- Competitive compensation, equity, and comprehensive health and wellness benefits.