Senior Data Scientist

AMRADNET US; United States; On-site; USD 180,000 - 320,000; Full time

Job summary

AMRADNET US seeks an expert-level Data Scientist to lead state-of-the-art research and develop multimodal imaging models for the diagnostic intelligence platform. You will drive concept-to-production in clinical environments, collaborating with radiologists and engineers to deliver clinically meaningful AI tools.

The role demands a PhD or MSc, deep medical imaging experience, and proficiency in PyTorch/TensorFlow, data governance, and scalable ML systems.

Qualifications

• Master’s or PhD in CS/ML/AI/biomedical engineering or closely related field.
• Expert proficiency in PyTorch, TensorFlow, Ultralytics YOLO, MONAI and Hugging Face ecosystems.
• Experience with medical imaging datasets, DICOM workflows, PACS, and radiology modalities.

Responsibilities

• Lead ideation, research, and development of models across CT, MRI, X-ray, ultrasound, and multimodal fusion.
• Architect and execute deep academic-level research with novel designs and validation.
• Translate research outputs into production-ready systems for clinical workflows.
• Build pipelines for dataset ingestion, labeling, augmentation, multimodal alignment, and large-scale imaging data management.
• Conduct expert-level model evaluation, error analysis, robustness testing, and optimization for inference.
• Ensure workflows meet high regulatory standards including data governance and reproducibility.
• Collaborate with engineering, product, and clinical teams to deliver clinically meaningful models.

Skills

Medical imaging expertise

Deep learning research

Production deployment

Regulatory compliance

Multimodal architectures

Data governance

Education

Master’s Degree

PhD

Tools

PyTorch

TensorFlow

Ultralytics YOLO

MONAI

Hugging Face

FiftyOne

Roboflow

Labelbox

V7

Encord

ONNX Runtime

NVIDIA TensorRT

Docker

Kubernetes

Azure AWS GCP

Job description

Seeking an expert‑level Data Scientist to lead state‑of‑the‑art research and develop advanced multimodal imaging models for our diagnostic intelligence platform. This role is designed for a senior practitioner with deep experience in medical imaging, radiology workflows, and machine learning research — someone capable of driving innovation from concept and academic rigor through to production‑grade deployment in clinical environments.

Responsibilities

• Lead ideation, research, and development of next‑generation models across CT, MRI, X‑ray, ultrasound, and multimodal fusion architectures.
• Architect and execute deep academic‑level research, including novel model designs, benchmarking, ablation studies, and scientific validation.
• Translate research outputs into production‑ready systems, ensuring reliability, scalability, and clinical relevance for real‑world diagnostic workflows.
• Build and oversee pipelines for dataset ingestion, cleaning, labeling, augmentation, multimodal alignment, and large‑scale imaging data management.
• Conduct expert‑level model evaluation, error analysis, robustness testing, and optimization for high‑performance inference.
• Ensure all workflows meet high regulatory standards, including data governance, auditability, reproducibility, and compliance expectations for medical AI.
• Collaborate closely with engineering, product, and clinical teams to deliver models that meaningfully advance diagnostic intelligence and clinical decision support.

Required Experience

• Master’s Degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, Biomedical Engineering, or a closely related technical field.
• Expert proficiency in PyTorch, TensorFlow, Ultralytics YOLO, MONAI, and Hugging Face ecosystems.
• Demonstrated experience with medical imaging datasets, DICOM workflows, PACS systems, and radiology modalities.
• Hands‑on use of FiftyOne, Roboflow, Labelbox, V7, or Encord for dataset curation, annotation, and quality control.
• Proven ability to lead research initiatives and publish or present advanced imaging work at academic or industry venues.
• Strong background in segmentation, detection, multimodal architectures, and imaging‑specific preprocessing pipelines.
• Experience deploying models using Core ML, ONNX Runtime, NVIDIA TensorRT/DeepStream, or major cloud ML platforms (Azure, AWS, GCP).
• Solid engineering foundation in Python, GPU computing, Docker/Kubernetes, and scalable ML systems.
• Familiarity with regulatory frameworks relevant to medical AI, including data privacy, auditability, and reproducibility standards.

Preferred

• Prior collaboration with radiologists, imaging scientists, or clinical imaging teams.
• Experience building multimodal (vision + text) diagnostic or triage models.
• Experience working in a start‑up company and small teams to develop big ideas that will have a global impact.