Published peer-reviewed research analyzing AI-driven automation across industries, evaluating efficiency trade-offs, workforce impact, and governance risks using structured industry datasets.

Built supervised machine learning models and conducted structured validation using confusion matrices and cross-validation techniques. Performed data preprocessing (handling missing values, feature engineering, encoding) to improve model accuracy and robustness. Analysed model performance gaps and identified root causes of misclassification to improve reliability.

Conducted exploratory data analysis (EDA) to identify operational inefficiencies and performance bottlenecks within structured datasets. Cleaned and transformed datasets to ensure accuracy, consistency, and usability for business reporting. Built KPI-aligned dashboards translating analytical findings into actionable insights for stakeholder decision-making.

Analysed ESG and sustainability datasets to support net-zero operational initiatives. Applied scenario-based modelling to assess impact trade-offs between environmental targets and operational feasibility. Developed data-backed recommendations aligned with business KPIs and regulatory considerations.

Built LLM skill extraction and resume-role matching; computed compatibility + readiness scores from job-market data. Deployed Streamlit dashboard for company comparisons + skill-gap roadmap.