Responsibilities of a Data Science Intern 

Responsibilities w.r.t Customer:

1. Communicating with Customers & Internal Stakeholders to understand the problem statement.
2. Design a Solution that aligns the Problem Statement, Solution Details, Output, Success Criteria, and solution's impact.
3. Designing solution.
   1. Feature Engineering: Identify features that would matter and ensure that the logic for feature selection is transparent & explainable.
   2. Model Selection: Use Pre-trained Models, AutoML, APIs or individual algorithms & libraries for effective model selection for optimal implementation.
   3. Optimise the model to increase its effectiveness with proper data cleansing and feature engineering refinements.
   4. Deploy the model for Batch or real-time predictions using methodologies like MLOps.
   5. Display or export the output into a visualisation platform.
   6. Document and back the decisions made throughout the problem-solving process.
4. Keep the promises made to the customer in terms of deliverables.
5. Maintaining and Updating Project execution trackers and documentation.
6. Create a POC for providing the customer with quick data insight.

Innovation and Asset building Responsibilities

1. Understand the current Solutions and Deployments to package 
2. Create clear documentation on architecture and design concepts used in the project.
3. Maintain coding standards and documentation for reusability.
4. Understand Current and new Cloud Services relevant to Data Science and stay up to date with the innovations in data science across competitors and open-source domains.
5. Proactively perform POCs to get hands-on experience with new technologies, including Google Cloud tools designed for Data Science applications.
6. Explore the usage of data science in various business and web analytics applications.

Mandatory Skills: 

• Python, SQL, Linux, CLI, Git
• Libraries and Frameworks: NumPy, Pandas, Scikit-learn, TensorFlow/Keras, PyTorch, XGBoost/LightGBM.
• Regression, Time series, Clustering, Neural Networks, Classification.
• Statistics: Descriptive, Inferential, Hypothesis testing, Distributions.
• Structured and Unstructured data manipulation with Python and SQL.
• Experience in Predictive modelling solutions.
• Cloud (Good to have): Object Storage, Compute, Data Warehousing, CI/CD.
• GenAI: Exposure with LLMs.