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Data Analysis & Feature Engineering: Collect, clean, and transform structured and unstructured datasets. Perform feature engineering and exploratory data analysis to derive meaningful insights.
Model Development: Build and deploy machine learning models for classification, regression, clustering, and anomaly detection using libraries such as scikit-learn, PyTorch, and TensorFlow.
Medical Imaging: Apply computer vision and deep learning methods for processing DICOM and NIfTI imaging data, using tools like MONAI, TotalSegmentator, and nnU-Net.
NLP & Unstructured Data: Implement natural language processing techniques for extracting insights from clinical texts, EMRs, and other unstructured data sources.
Data Visualization: Communicate results through dashboards and visualizations using tools such as Tableau, Plotly, or Power BI.
AI Integration: Collaborate with engineering and product teams to integrate AI modules into full-stack applications and real-time systems.
Experimentation: Design, run, and analyze A/B tests and other experimental frameworks to evaluate model and product effectiveness.
Model Monitoring: Monitor model performance post-deployment, retrain models when needed, and maintain ML pipelines using versioning and tracking tools like MLflow or DVC.
Solution Design: Propose data-driven strategies and solutions to business challenges through quantitative modeling and forecasting.
Continuous Learning: Stay updated with the latest trends in AI, machine learning, and data science, and apply new techniques to production workflows.
Medical Imaging Knowledge: Experience working with medical datasets (CT, MRI, X-ray), understanding of imaging formats (DICOM/ NIfTI), nanonet and use of medical imaging libraries. (Optional skill)
eNest Technologies Pvt Ltd
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