GenAI Data Scientist

Description:
 

• Formulate, design, and deliver AI/ML-based decision-making frameworks and generative models for business outcomes.
• Develop and fine-tune AI models for NLP tasks, such as summarization, Named Entity Recognition (NER), text classification, and sentiment analysis, focusing on unstructured clinical records.
• Implement dynamic prompt engineering strategies to optimize generative AI model outputs and improve overall performance.
• Analyze and preprocess large datasets, especially unstructured medical records (e.g., physician notes, discharge summaries), using libraries like Pandas, NLTK, and SpaCy.
• Conduct experiments to evaluate AI model performance, utilizing metrics such as precision, recall, and F1-score, and continuously improve models through hyperparameter tuning.
• Collaborate with cross-functional teams, including data scientists and software engineers, to integrate AI models into cloud-based production environments (e.g., AWS, Azure).
• Incorporate human-in-the-loop feedback to refine AI models and improve outcomes for clinical use cases.
• Stay updated with the latest research and advancements in AI and NLP, applying cutting-edge techniques such as transfer learning, attention mechanisms, and fine-tuning pre-trained models.
• Design and implement data pipelines, structuring both SQL and NoSQL databases (e.g., PostgreSQL, MongoDB) for efficient data storage and retrieval.
• Deploy AI models using cloud platforms (AWS, Azure) and containerization (Docker), leveraging CI/CD pipelines for scalability and performance optimization.

Requirements:
 

• 6+ years of experience in AI/ML development with a strong focus on NLP and Generative AI, using frameworks such as TensorFlow, PyTorch, and Hugging Face.
• Mastery in Python and proficiency in libraries such as Transformers, NLTK, SpaCy, and Gensim, with experience in data manipulation using Pandas and NumPy.
• Demonstrated expertise in generative AI models (e.g., OpenAI’s GPT, LLaMA) and libraries like VLLM.
• Experience in prompt engineering strategies for generative AI model enhancement.
• Familiarity with cloud platforms (AWS, Azure) and containerization (Docker), with hands-on experience deploying machine learning models using CI/CD pipelines.
• Experience with human-in-the-loop systems, integrating feedback from clinicians to refine AI models.
• Strong analytical and statistical modeling skills, with experience evaluating model performance and iterating on improvements.
• Experience working with healthcare data standards such as HL7, FHIR, ICD codes, and SNOMED.
• Familiarity with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Elasticsearch) databases, and experience optimizing them for AI model integration.
• Ability to articulate technical challenges and solutions effectively, with strong written and verbal communication skills.
• Master’s degree in Data Science, AI, Computer Science, or a related field + 10 years of experience, or PhD + 4 years.