Data Science Intern

Role Overview
We are seeking a motivated Data Science Intern to join our team. In this role, you will
contribute to various stages of the data science pipeline—from data collection and
exploratory analysis to model development and experimentation. You will have hands-on
exposure to advanced machine learning techniques, AI agent development, and
cutting-edge AI/ML research, especially as it relates to the Healthcare sector. This is an
excellent opportunity to gain real-world experience and collaborate with a
cross-functional team of data scientists, engineers, and domain experts.

Key Responsibilities

1. Data Collection & Preparation Data Gathering: Participate in data collection and cleaning processes, working with structured (tabular), unstructured (text, images), and time-series data sources. Preprocessing & Feature Engineering: Explore datasets to identify relevant features, engineer new variables, and handle outliers or missing values to enhance model performance.
2. Exploratory Data Analysis (EDA) Data Analysis: Conduct in-depth analyses of datasets to uncover trends, patterns, and anomalies. Data Visualization: Use libraries and visualization tools (e.g., Matplotlib, Seaborn, Plotly) to present insights that inform model-building decisions.
3. Model Training & Tuning Algorithm Selection & Training: Train and evaluate a variety of machine learning models (CNNs, GANs, Multi Modal models) using popular frameworks (e.g., TensorFlow, PyTorch). Hyperparameter Optimization: Fine-tune models to maximize performance metrics such as accuracy, precision, recall, or F1-score. Architecture Exploration: Learn and experiment with emerging model architectures, including large language models (LLMs) where relevant.
4. AI Agent Development (Healthcare Focus) AI Agent Implementation: Assist in developing specialized AI agents for Healthcare-related tasks. Reinforcement Learning: Help design and implement reinforcement learning pipelines for training AI agents on real-world or simulated tasks. Systems Integration: Contribute to integrating AI agents into existing applications or platforms.
5. Research & Development Stay Current: Keep up with the latest advancements in Healthcare AI and machine learning technologies. Experimentation: Explore and test novel algorithms, libraries, and techniques to improve model performance and efficiency. Innovation: Share new ideas and approaches to push the boundaries of AI/ML solutions, particularly in the Healthcare domain.