About Me

I am a Data Scientist with a background in Artificial Intelligence and Data Analytics based in the UK, specialising in machine learning, model evaluation, and explainable AI. I focus on building reliable, interpretable systems that support real-world decision making in healthcare and finance.

My work combines structured data analysis, feature engineering, robust validation, and SHAP-based explainability to ensure models are not only accurate, but transparent and defensible. I care about clarity, rigor, and building solutions that can actually be trusted.

I am particularly interested in responsible AI, model validation in high-stakes environments, and building systems that balance performance with transparency.

Building Trustworthy AI

I design machine learning systems with a focus on reliability, interpretability, and real-world impact. My work sits at the intersection of technical depth and responsible AI, where performance is important but transparency is essential.

With a background in Artificial Intelligence and Data Analytics, I have worked on healthcare risk modelling, explainable financial decision systems, and interactive analytics dashboards. I approach each project methodically, from problem definition and feature engineering to rigorous validation and clear model interpretation.

I focus on designing systems that can be validated, audited, and clearly explained to both technical and non-technical stakeholders.

Technical Skills

 

  • Machine Learning (classification, model validation, cross-validation)

  • Explainable AI (SHAP, model interpretability)

  • Data Processing (feature engineering, SQL, data cleaning)

  • Deployment & Dashboards (Streamlit, interactive analytics)

  • Responsible AI & Evaluation Frameworks

 

Technical Expertise

Core Areas

  • Machine Learning & Predictive Modelling

  • Explainable AI and Model Interpretation (SHAP)

  • Model Validation & Cross-Validation Frameworks

  • Responsible and Trustworthy AI

Technical Stack

  • Python (scikit-learn, XGBoost, pandas, NumPy)

  • SQL for structured data analysis

  • Streamlit for interactive dashboards

  • Data preprocessing and feature engineering

  • Statistical evaluation and performance metrics

     

    Let's Connect!

    I am open to roles in data science, machine learning, and research-focused AI systems, particularly in healthcare, pharmaceutical, finance, and high-stakes decision environments.

    For collaboration or opportunities, feel free to connect via LinkedIn or email.

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