GenAI Data Scientist – Medical Imaging, RIS & Healthcare Services

Role Overview

We are seeking a GenAI Data Scientist with strong expertise in medical imaging, radiology workflows (RIS/PACS/VNA), and service-oriented healthcare platforms. This role will focus on designing, training, and deploying Generative AI and Agentic AI solutions that improve clinical efficiency, operational intelligence, reporting automation, and service optimization across healthcare systems.

You will work closely with product managers, clinical SMEs, engineering teams, and cloud architects to translate healthcare problems into scalable AI-driven solutions.

Key Responsibilities

1. Generative AI & ML Development

• Design and develop LLM-powered and multimodal AI solutions for:

  • Radiology reporting automation

  • Imaging analytics and insights

  • Clinical decision support

  • Operational and service intelligence

• Build agentic AI workflows for tasks such as:

  • Study triage and prioritization

  • Report quality checks

  • Workflow optimization across RIS/PACS/VNA

• Fine-tune and evaluate LLMs and vision-language models using domain-specific medical datasets.

2. Medical Imaging & Radiology Domain Applications

• Work with DICOM, non-DICOM, and multimodal data (images, text, metadata, audio/video).

• Develop AI models for:

  • Image understanding and feature extraction

  • Metadata enrichment and study classification

  • Automated measurements and annotations

• Collaborate with clinical experts to ensure clinical relevance, safety, and interpretability of AI outputs.

3. Data Engineering & Model Lifecycle

• Build robust data pipelines for ingesting data from RIS, PACS, VNA, and service platforms.

• Perform data curation, labeling strategies, feature engineering, and dataset versioning.

• Implement model evaluation, monitoring, drift detection, and continuous learning pipelines.

4. Healthcare Services & Operations Intelligence

• Apply AI to non-clinical service use cases, including:

  • Turnaround time (TAT) optimization

  • Resource utilization and scheduling

  • SLA adherence and predictive alerts

  • Revenue leakage and operational bottlenecks

• Build AI-driven dashboards, summaries, and conversational analytics for executives and operations teams.

5. Deployment, MLOps & Cloud

• Package and deploy AI models as APIs and microservices.

• Implement MLOps best practices:

  • CI/CD for models

  • Model registry and versioning

  • Observability and performance tracking

• Work on cloud-native deployments (AWS / GCP / Azure), ensuring scalability, security, and compliance.

6. Compliance, Ethics & AI Safety

• Ensure solutions comply with HIPAA, GDPR, and healthcare data privacy standards.

• Implement explainability, auditability, and bias mitigation in AI models.

• Participate in AI governance and responsible AI initiatives.