Data Scientist In LLM Multimodal Healthcare Job (Remote)

Job Description:
We are seeking a highly skilled Data Scientist and contribute to groundbreaking advancements in LLM multimodal healthcare. The ideal candidate will have a strong foundation in data science methodologies and a deep understanding of LLM applications, particularly in the context of radiology, visual images, and text.
Key Responsibilities:
Develop and implement innovative LLM models for analyzing and interpreting radiology images, text data, and other relevant healthcare information.
Collaborate with cross-functional teams to identify and address complex healthcare challenges using advanced data science techniques.
Explore and apply state-of-the-art LLM architectures and methodologies to enhance the accuracy and efficiency of our models.
Contribute to the development of intellectual property (patents, publications) related to LLM applications in healthcare.
Qualifications:
Advanced degree in Computer Science, Data Science, Statistics, or a related field.
6+ years of hands-on experience in data science, with a strong focus on LLM applications.
Proven expertise in working with large-scale datasets, including radiology images, text data, and other healthcare-related information.
Deep understanding of LLM architectures, training methodologies, and evaluation metrics.
Proficiency in Python, R, or other relevant programming languages.
Experience with cloud-based platforms (e.g., AWS, GCP, Azure) and data science tools (e.g., TensorFlow, PyTorch, Scikit-learn).
Strong problem-solving, analytical, and communication skills.
Preferred Qualifications:
Experience working on cancer-related projects.
Familiarity with healthcare data standards and regulations (e.g., HIPAA, GDPR).
A track record of obtaining patents or publishing research papers in the field of data science or healthcare.
Graduation from a prestigious institution such as IIT, NIT, IIM, or BITS Pilani.

Information above is Internshala's interpretation and paraphrasing of what we found on the shared link.