Data Scientist

Role: Data Scientist – Generative & Agentic AI (Healthcare Domain)

Educational Qualification: ME / BE / MCA

Experience Required: 4+ Years

Shifts: Day Shift

Skills & Responsibilities

➤ Experience:

4+ 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.

 Effective communication for presenting results, writing technical documentation,

and supporting regulatory submissions.

 Knowledge of computer vision is a plus for multimodal applications (e.g.,

diagnostics, image-text synthesis).

Interested candidates can share there cv

shivangi.manandhaR@etenico.com