H S

Bioengineering graduate with hands-on experience in machine learning, statistical analysis, and applied generative AI (LLMs), combining Python-based data science (Pandas, NumPy, Scikit-learn) with domain knowledge of clinical research and healthcare data workflows. Experienced building dashboards (Power BI) and end-to-end AI applications, with a strong foundation in statistics and probability. Comfortable working cross-functionally and communicating technical findings to non-technical audiences.

Seeking an entry-level AI engineer / data science role in healthcare technology.

Data Science Intern at Integrated Cancer Treatment & Research Centre (2026-01 – 2026-06)

• Analysed EORTC QLQ patient-reported outcome data using non-parametric statistical tests (Mann-Whitney U, Friedman) to compare study and control groups, applying structured statistical analysis and feature engineering to derive clinically actionable insights from complex healthcare datasets.
• Presented findings to clinical research staff, translating statistical/model output into plain-language conclusions for a non-technical audience.
• Tools: Python, R, NumPy, Pandas, Power BI and Excel.

Biomedical Engineering Intern at Quantum Leap Health / MIT (2025-06 – 2025-08)

• Contributed to development of a wearable maternal-health monitoring device: supported sensor integration, prototype testing, and structured data collection and signal processing within an established engineering framework.

Bachelor of Technology in Bioengineering – MIT Art, Design & Technology University (2022-09 – 2026-06)