Ehimen Agbonkhese

Data Analyst and Computer Science graduate with experience, analysing structured, unstructured datasets. Skilled in data-driven decision making. Skilled in Python, SQL, Power BI, Excel, and data visualisation, with experience developing data pipelines, improving data quality, and producing analytical insights.

Strong communicator with excellent collaborative skillset, managing tasks and bringing them to conclusion, in a team or as a leader. I've used my previous experience in problem solving to apply analytical and reporting skills, within Artificial Intelligence infrastructure to improve accessibility and navigation in the tertiary educational environments.

Chatbot using retrieval augmented generation

(2024-09)

Designed and developed a Retrieval Augmented Generation (RAG) platform to improve access to departmental information using structured and unstructured datasets.

• Collected, cleaned, transformed, and validated large volumes of departmental data from multiple online and document-based sources.
• Developed automated web scraping workflows using Python and BeautifulSoup to extract and structure data for analysis and retrieval.
• Built and maintained a scalable data pipeline to process, categorise, and embed datasets into a vector database (Pinecone).
• Conducted comparative analysis on embedding models to test retrieval accuracy, and processing efficiency under performance constraints.
• Created a test dataset in Excel to validate system accuracy and measure performance across multiple information categories.
• Performed analytical testing and evaluation of system outputs to identify improvements in retrieval relevance and response quality.
• Presented findings and demonstrated the application through a Streamlit interface for both technical and non-technical users.
• Collaborated with the supervisor to refine technical requirements, improve usability, and ensure alignment with user needs.

Intern - London Underground

(2021-09 - 2022-09)

• Supported operational teams within service control and station environments across the London Underground network.
• Assisted with workflow monitoring and communication of real-time operational updates to staff and passengers.
• Observed operational decision-making processes and gained understanding of transport service coordination and performance management.
• Developed communication and stakeholder engagement skills within fast-paced operational environments.

BSc - Computer Science - Queen Mary University of London (2022-09 - 2025-09)

A levels - Mathematics, Chemistry, Computer Science - The Palmer Catholic Academy (2015-09 - 2022-06)

GCSE - General Education - The Palmer Catholic Academy (2015-09 - 2022-06)

St Joseph's Primary School (2007-09 - 2015-07)