Data Scientist Generative Ag AI Healthcare Domain

Job description

Role: Data Scientist Generative & Agentic AI (Healthcare Domain) Educational Qualification: ME / BE / MCA Experience Required: 2+ Years Shifts: Day Shift Skills & Responsibilities Experience: 2+ years of experience in Machine Learning, Deep Learning, or Generative AI, with a strong focus on healthcare software product development and medical coding automation. Programming & Frameworks: • Proficient in Python, with hands-on experience using Pandas, NumPy, and OOPs concepts. • Practical experience with PyTorch, TensorFlow, Keras, and Hugging Face Transformers. • Familiar with writing optimized SQL queries for large-scale structured clinical data. Healthcare-Specific AI: • Strong understanding of medical coding standards (ICD, CPT, SNOMED), EHR systems, and clinical document processing. • Exposure to HL7, FHIR APIs, and privacy regulations like HIPAA is an added advantage. Generative AI & NLP: • Experience working with LLMs, GANs, VAEs, and Diffusion Models in healthcare use cases (e.g., clinical summarization, automated coding, documentation assistance). • Familiar with Azure OpenAI, AWS Bedrock, DALL•E, and Stable Diffusion platforms. • Strong grasp of NLP techniques such as Named Entity Recognition (NER), token classification, contextual embeddings, and deep learning models like RNN, LSTM, GRU. Agentic AI & Autonomous Workflows: • Experience or familiarity with building agentic systems using LangChain, AutoGen, or CrewAI for orchestrating multi-step tasks (e.g., claim validation, document parsing). • Ability to integrate autonomous agents with tool-based systems and APIs to enhance workflow efficiency. Machine Learning & Statistical Modeling: • Expertise in supervised and unsupervised ML, including Random Forest, SVM, Boosting, Bagging, Regression, and Clustering methods. • Strong capability in feature engineering, model training, and cross-validation for healthcare data. MLOps, Deployment & Data Integration: • Experience with cloud platforms such as AWS, Azure, or GCP for scalable ML model deployment. • Familiarity with MLOps practices, CI/CD pipelines, Docker, Kubernetes, and model versioning. • Hands-on experience with Apache NiFi for data ingestion, integration, and workflow automation, including designing NiFi flows for structured/unstructured clinical data and seamless integration with downstream ML models. • Proficient with Linux systems and GPU-based ML workflows. Research, Compliance & Ethics: • Experience contributing to AI research, open-source projects, or Kaggle competitions focused on healthcare or NLP. • Awareness of AI ethics, bias mitigation, explainability techniques, and safe deployment of AI in clinical settings. Soft Skills & Collaboration: • Proven ability to work independently and in agile teams with product managers, clinical SMEs, and backend engineers.