Data Scientist
Technical Skills: Python, R, SQL
Education
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MSc. Information Systems Kingston University (November 2025) -
MSc. Data Science Middlesex University (November 2023) -
BSc. Information Technology Middlesex University (July 2021)
Work Experience
Data Science Consultant @ Amdari (February 2025 - Present)
- Supported delivery of data analysis and modelling workstreams for financial services clients, working with datasets ranging from tens of thousands to low hundreds of thousands of records.
- Contributed to customer segmentation analysis using clustering and descriptive statistics, helping teams better understand customer groups and behavioural patterns.
- Assisted in the development and testing of predictive models for insurance cost estimation, supporting more consistent and repeatable analytical outputs.
- Performed data preparation, feature engineering, and exploratory data analysis, reducing data quality issues identified during modelling by 20–25%.
- Collaborated with consultants, data engineers, and stakeholders to align data requirements, analysis outputs, and reporting needs across project phases.
- Communicated analytical findings through presentations and written summaries, supporting business discussions and decision-making.
Data Scientist @ Nigerian Ports Authority(August 2022 - January 2025)
- Applied data analysis and statistical techniques to large operational and logistics datasets, supporting data-driven decision-making across port operations.
- Built and maintained structured datasets covering daily vessel traffic, turnaround times, cargo volumes, and resource utilisation (100k+ records).
- Developed automated reporting and dashboards using Excel, SQL, and Power BI, reducing manual reporting effort by 40% and improving data visibility for management.
- Analysed operational performance metrics to identify inefficiencies, contributing to process improvements that reduced delays and turnaround time by 20%.
- Performed data cleaning, validation, and standardisation, improving overall data quality and consistency by 30%.
- Collaborated with cross-functional teams to translate operational requirements into analytical outputs and actionable insights.
Projects
Risk Classification Thesis Project
Developed a script using Python to fetch section 1.A (Risk Factors) of the 10-K filings from the EDGAR Database. Researched several NLP manipulation techniques to clssify the risk factors including Linear SVC, Naive Bayes, Logistic Regression, Topic Modelling, Neural Networks and Neural Topic Modelling. With the rise of LLMs (Large Language Models), that was the best solution to the risk classification problem but it costs more. AI Solution
AI Risk Classification
Built an automated pipeline to extract risk factors from U.S. SEC 10-K filings and classify them with language models into risk categories. Ingests text from filings, preprocesses risk text, and applies NLP/LLM classification to assign risk category labels. Technologies: Python, Hugging Face/LLM API (or similar), NLP preprocessing (tokenization, vectorization), structured output classes for risk taxonomy.
FNOL(First Notice Of Loss) Intelligence System
Developed a machine learning model to predict claim outcomes/costs based on First Notice of Loss (FNOL) insurance data. Workflow included cleaning and transforming structured incident/claim features, exploratory data analysis, and supervised ML training with evaluation metrics. Techniques likely used: Python, pandas/NumPy, Scikit-learn models (e.g., regression/classification), feature engineering, model validation.
Customer Segmentation using RFM Analysis
Performed customer segmentation of banking customers using RFM (Recency, Frequency, Monetary) analysis to score and cluster clients by value/behavior. Computed RFM metrics from transactional data, then applied clustering (e.g., K-Means) to derive segments for targeted marketing. Technologies: Python, pandas for data manipulation, Scikit-learn for clustering, visualization libraries for segment profiling.
AI Bed Prediction
Developed a machine learning system to predict hospital bed demand using historical hospital utilization data, enabling proactive capacity planning and operational decision-making. Built a full data science workflow including data preprocessing, feature engineering, and predictive modeling to forecast future bed requirements. Technologies and techniques: Python, Jupyter Notebook, pandas and NumPy for data manipulation, exploratory data analysis, supervised machine learning models for demand prediction, and performance evaluation using regression metrics.
Travel Assistant AI
Built an AI-powered conversational chatbot designed to handle hotel-related queries, enabling users to interact naturally for information retrieval and basic booking assistance. The system processes user input, interprets intent, and generates context-aware responses to simulate a hotel assistant experience. Technologies and techniques: Python, natural language processing, large language models (LLMs) for conversational response generation, prompt engineering, and integration frameworks such as LangChain and API-based LLM services for dialogue management and response orchestration.
Shift Perfomance Optimisation
Developed a data analysis and predictive modelling solution to evaluate and improve workforce shift performance using operational data. The project processes shift-level data to identify patterns in productivity, inefficiencies, and performance variability, then applies analytical and machine learning techniques to generate insights and performance predictions. Technologies and techniques: Python, pandas and NumPy for data preprocessing, exploratory data analysis (EDA) for performance trend identification, feature engineering on time/shift-based variables, and supervised machine learning models (via scikit-learn) to assess and predict performance outcomes.