Elisha Fernandes

Biomedical Engineering student with industry experience developing dashboards, analytical models, and reporting tools that supported strategy, operational performance, and management decision-making in a fast-paced, regulated environment at Unilever. I have used Power BI, Excel, Python, SQL/Kusto Query Language, and data-quality checks to turn complex operational datasets into clear, actionable insights, including automated gas alarm monitoring, live energy performance dashboards, and structured reporting tools for safety, operations, and management stakeholders. My degree has strengthened my quantitative foundation through data science, regression modelling, biomedical image analysis, signal processing, and technical problem-solving.

I am particularly interested in healthcare analytics, finance, risk, and quantitatively heavy roles where data can be used to improve outcomes, influence decisions, and create measurable impact. I have also been exploring quantitative finance through financial and risk simulations and WorldQuant BRAIN, where I have worked on alpha ideas using time-series analysis, ranking, neutralisation, and performance metrics such as Sharpe ratio, turnover, and fitness.

Project Engineer (Placement) at Unilever (2024-06 – 2025-06)

Led technical projects and system upgrades during year-in-industry placement

• Led the technical upgrade of the Intravis Quality Gate system, achieving a sub-one-month ROI and slashing quality costs by 87% (£81,000 to £10,000)
• Streamlined energy monitoring for heat tunnels by developing a live Power BI dashboard that integrated SCADA data to visualise cumulative trends and improve operational transparency
• Identified non-compliance patterns across multiple shifts by performing a comprehensive audit of mandatory safety checks through the development of complex Excel Pivot Tables
• Optimised energy benchmarking by discretising continuous signal data to develop scatter models linking consumption to volumes, allowing for the precise identification of overconsumption anomalies
• Modernised data infrastructure by replacing a legacy SQL/PHP model with a high-performance Kusto Query Language (KQL) solution in Azure to clean and transform real-time telemetry
• Improved data accuracy by 150% by designing a Power BI dashboard that translated processed data into intuitive visualisations, enabling proactive safety decision-making

Mobile Phone Spectrometer App (Backend) | September 2025 – Present

• Built a modular Python backend for spectral analysis that handled the data lifecycle from raw image The application is a chlorophyll and Bilirubin assessment

BEng in Biomedical Engineering with Year in Industry – Queen Mary University of London (2022-09 – 2026-06)

All India Senior School Certificate Examination in General Education – India International School in Japan (2019-06 – 2021-06)