Applied AI • Data Science • NLP • Speech Analytics
Applied AI & Data Science professional with 7+ years across insurance and analytics. I build end-to-end ML and NLP systems — from data pipelines to evaluation and stakeholder-ready outputs. Currently pursuing an MSc in Data Science & AI at the University of Liverpool (Jan 2026).
Selected projects. Replace the “Repo” links below with your exact GitHub repository URLs.
End-to-end system that transforms call audio into speaker-attributed transcripts, concise summaries, and sentiment signals to support QA and escalation workflows.
Custom and pretrained CNN experiments with robust evaluation protocol, error analysis, and reporting for multi-class image classification.
Structured ML workflow: EDA → feature engineering → model comparison → evaluation. Built for clarity, reproducibility, and decision-ready interpretation.
Spatial analysis and dashboarding combining environmental and demographic datasets to surface actionable insights and patterns.
I’m focused on building practical AI systems that work with real constraints: noisy data, operational needs, and stakeholders who need clarity. My background blends analytics engineering, automation, and applied ML/NLP.
I enjoy turning ambiguous problems into measurable deliverables — pipelines, models, dashboards, and clear narratives.
A focused snapshot of tools I use most.
Python • SQL • Pandas • NumPy • Git
scikit-learn • Feature Engineering • Model Evaluation • XGBoost
ASR (Whisper/Faster-Whisper) • Sentiment • Summarisation • NER
Power BI • Excel • Snowflake • AWS (Redshift/Athena/Glue)
If you’d like to discuss a role, collaboration, or want a walkthrough of a project, reach out.