CHRISTIAN OBIECHINA

Developed Python-based STEM problems and reproducible workflows to evaluate AI reasoning, boosting student engagement by 50% and improving model accuracy by 25% via advanced NLP integrations.

Provided AI and data science solutions including predictive modelling, NLP and deep learning for clients across public sector, healthcare, retail and manufacturing, improving data processing efficiency by 20% and delivering 10+ deployed ML models.

Tutored students in mathematics, statistics, data science and programming, increasing student performance by 30% and retention by 40% through personalized learning plans and feedback-driven instruction.

Built predictive models and managed >5TB of market and traffic datasets to classify valuation regimes and forecast regime shifts, boosting client satisfaction by 25% with customized AI solutions.

Processed and cleaned 1.6 million records and developed deep learning models achieving 98% accuracy to predict outcomes and validate research hypotheses, improving model generalizability by 15%.

Coordinated operations, schedules and resources to support project execution, improving client satisfaction by 30% and reducing energy costs by 20% through sustainable practices.

Activities and societies: MSc research project: Global Diurnal Sentiment Analysis (large-scale Twitter case study); presentations on deep learning results and methodology.

Completed an MSc in Artificial Intelligence and Data Science with a research project applying deep learning to a 1.6 million–record dataset achieving high predictive accuracy.

Activities and societies: Census Data Predictive Analysis project; presentations on feature selection and model performance.

Earned a Bachelor of Engineering in Electronic and Computer Engineering, conducting predictive modelling work using census data.