Description :

- Develop and implement AI-centric models and analytics systems to support healthcare professionals in decision-making and patient care.

- Process, clean, and verify the integrity of large and complex datasets to ensure data reliability and consistency.

- Perform data mining and statistical analysis using state-of-the-art methods and machine learning techniques.

- Enhance data collection frameworks to capture additional features and signals relevant for model development.

- Integrate external data sources to enrich existing datasets and improve model accuracy and insight generation.

- Lead end-to-end model development - from data gathering, feature engineering, and model training to deployment and productization.

- Apply machine learning, NLP, and predictive analytics techniques for diagnosis, risk assessment, and forecasting applications.

- Fine-tune large language models (LLMs) to improve performance for healthcare-specific use cases.

- Collaborate with engineering, product, and clinical teams to align data science solutions with business and medical objectives.

- Create data visualizations and dashboards to communicate findings effectively to stakeholders and leadership.

- Stay updated on the latest advancements in AI/ML frameworks and healthcare data science, and drive innovation within the team.

Required Skills and Qualifications :

Education :

- B.Tech or higher in Computer Science, Data Science, Statistics, or a related discipline.

Experience :

- 25 years of experience in data science, analytics, or applied machine learning.

- Strong foundation in statistics, probability, and machine learning algorithms (e.g., k-NN, Naive Bayes, Decision Forests, Neural Networks).

- Hands-on expertise in Python or R, with experience using libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, Keras, or PyTorch.

- Proficiency in SQL and experience with databases such as PostgreSQL or MongoDB.

- Experience in end-to-end ML lifecycle management - from data preprocessing to deployment.

- Strong understanding of forecasting techniques (e.g., ESM, ARIMA, ARIMAX, UCM) and predictive modeling.

- Working knowledge of data visualization tools such as Tableau or Power BI.

- Exposure to cloud platforms (AWS, GCP, or Azure) for scalable model deployment.

- Excellent communication and collaboration skills; ability to explain technical findings to non-technical stakeholders