Data Science Intern

About the Role

• Extract actionable intelligence from complex datasets
• Prototype machine learning models
• Build reproducible data pipelines supporting Xynvia’s healthcare and enterprise AI systems

Key Responsibilities

• Clean, explore, and preprocess structured and unstructured data to extract meaningful patterns and baseline metrics.
• Develop, evaluate, and benchmark machine learning and statistical models using sound validation techniques.
• Build and maintain reproducible data pipelines, ETL scripts, and analytical workflows for real-world datasets.
• Collaborate with engineering teams to package and serve model prototypes via lightweight APIs (FastAPI / Flask).
• Document analytical methodologies, dataset assumptions, validation results, and experiment findings with clarity.

Requirements & Tech Stack

• Proficiency in Python and core data libraries (Pandas, NumPy, Scikit-Learn)
• Strong foundation in statistics, hypothesis testing, probability, and exploratory data analysis
• Intermediate SQL for querying, aggregating, and structuring relational data
• Working knowledge of machine learning algorithms (regression, classification, clustering, ensemble methods)
• Familiarity with data visualization tools (Matplotlib, Seaborn, Plotly) and version control (Git)

Useful, not required: Experience with PyTorch, TensorFlow, NLP / LLM concepts, computer vision basics, vector databases, FastAPI, Docker, or PostgreSQL.

Qualifications

• At least one end-to-end data science project or a credible portfolio demonstrating exploratory data analysis (EDA), statistical modelling, feature engineering, and model evaluation.
• Show us how you think, what you have built or learned, and how you improve work after feedback.