Data Analysis, Business Intelligence Analysis, Data Science, Artificial Intelligence

Table of Contents

-Projects -Competencies -Education -Work Experience -Research Publications

Portfolio Projects

Data Visualization Project (PowerBI) - Myntra Product Catalog Project

Data Visualization Project (SQL, PowerBI, Excel) - Top UK YouTubers in 2024 Project

Data Exploration (SQL) - Covid Vaccination Data Exploration

Data Cleaning (SQL)- Housing Data Cleaning

Competencies

• Data Analysis: SQL, NumPy, Pandas, Scikit Learn, Excel
• Data Visualization: PowerBI, Tableau, Looker, Matplotlib, Seaborn
• Data Science/Machine Learning/AI: TensorFlow/Keras, Pytorch, Azure Open AI Service
• Data Engineering: Snowflake, Databricks, MS Azure, Google Cloud Platform
• Programming: Python, DAX
• Version Control: GitHub/Gitlab
• OS: Linux, Windows

Education

• Ph.D. Artificial Intelligence/Computer Science (Aug 2022): School of Computer Science and Engineering, Victoria University of Wellington, Wellington, New Zealand

• M.Eng. Digital Electronic Engineering (Aug 2017): Department of Electronic Engineering, University of Nigeria, Nsukka, Enugu, Nigeria

• B.Eng. Electronic Engineering (Jul 2013): Department of Electronic Engineering, University of Nigeria, Nsukka, Enugu, Nigeria

Work Experience

Senior AI & Data Consultant

Ernst & Young LLP | Wellington, New Zealand (Jun 2023 – Mar 2024)

• Furthered solution design within the firm through the design of Generative AI–based proof of concept for improving internal EY processes and for bidding on client work. This included prompt engineering and building AI apps with Python.
• Established the data-driven capabilities of a client organization through the definition, design, implementation, and testing of metrics and visual reporting build for multiple insightful PowerBI dashboards under stringent time constraints; which included the inaugural integration of diverse data sources on Snowflake
• Reduced rework in the product development process by 75% through agile development methods, rapidly wireframing/prototyping the specified requirements; fostering efficient collaboration with product owners.
• Significantly minimized ambiguity during product handover by developing detailed design documents to effectively communicate with and guide the client/stakeholder through each stage.
• Increased output by 25% through the establishment of standardized templates for every deliverable, ensuring consistency which guaranteed superior output quality throughout the team.

AI & Data Consultant

Ernst & Young LLP | Wellington, New Zealand (Jun 2022 – Jun 2023)

• Developed and executed SQL scripts to extract and analyze data from large databases for the creation of insightful reports that informed stakeholder decision-making process.
• Reduced backlog by 85% in 3 months by providing timely BAU (Business as Usual) support for a managed service and implementing a batching process for addressing issue tickets, incidents, and tasks.
• Reduced onboarding effort of new team members by 60% through maintaining knowledge bases of known issues and fixes and created documentation and manuals to ensure information is recorded, updated, and shared.
• Cultivated robust relationships with key stakeholders by approaching discussions with an open mind, and adeptly gathered requirements through insightful conversations and questions. Produced and presented monthly reports to summarise the operational activities of the key technical stakeholders/owners.
• Delivered coaching and mentorship, which contributed to the professional development and growth of consultants and interns and promoted morale in a constantly changing environment.

Artificial Intelligence Researcher

Victoria University of Wellington | Wellington, New Zealand (Apr 2019 - May 2022)

• Designed end-to-end NLP experiments to extract and construct informative features for a supervised text classification task (predictive modelling) to detect hate speech on social media platforms using algorithms such as LSTM, RNN, and CNN with tools like NumPy, Scikit-learn, TensorFlow, Keras, Pytorch and Python.
• Designed unsupervised NLP/machine learning experiments based on K-means clustering to identify the target of hate speech text. This reduced dependence on scarce and biased labelled data and introduced model explainability using tools like NumPy, Scikit-learn, TensorFlow, Keras and Python.
• Analyzed result data using Excel and Python, and published the statistically validated research results in peer-reviewed conference proceedings and journal articles.
• Presented research ideas and findings at 5 international conferences and 2 competitions using Matplotlib and Seaborn for results visualization.

University Lecturer

Department of Electronic Engineering, University of Nigeria, Nsukka | Enugu, Nigeria (May 2016 – Jan 2019)

• Analyzed experimental results from completed digital electronic engineering research for publications and conferences.
• Cleaned and analyzed thousands of rows of student examination result data to calculate individual course results and cumulative GPA.
• Digitized old student records for the alumni office and established validated data collection processes for new records.
• Designed exam timetables for four levels of departmental courses, ensuring no conflicts with faculty or inter-level courses. This allowed students to retake exams from different levels without scheduling conflicts.
• Instructed 3 undergraduate courses, fostered an interactive learning environment, and improved student participation and academic performance.
• Supervised 3 undergraduate students' projects, guiding students on methodology, execution and report writing standards leading to academic conference publications and presentations.

Research Publications

1. MADUKWE, K. J., GAO, X., & XUE, B. “Token Replacement-based Data Augmentation Methods for Hate Speech Detection” In World Wide Web: Internet and Web Information Systems Journal (Special Issue on Web Intelligence: Artificial Intelligence in the Connected World) 2022, pp 1129–1150.
2. MADUKWE, K. J., GAO, X., & XUE, B. “What Emotion Is Hate? Incorporating Emotion Information into the Hate Speech Detection Task”. In 18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Hanoi, Vietnam, pp 273–286.
3. MADUKWE, K. J., GAO, X., & XUE, B. “Dependency-Based Embedding for Distinguishing Between Hate Speech and Offensive Language,” 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2020, pp. 860-868.
4. MADUKWE, K. J., GAO, X., & XUE, B. “In Data We Trust: A Critical Analysis of Hate Speech Detection Datasets”. In Proceedings of the Fourth Workshop on Online Abuse and Harms(Online, 2020), Association of Computational Linguistics (ACL), pp. 150–161
5. MADUKWE, K. J., GAO, X., & XUE, B. “A GA-based Approach to Fine-tuning BERT for Hate Speech Detection”. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI)(2020), pp. 2821–2828.
6. MADUKWE, K. J., & GAO, X. “The Thin Line Between Hate and Profanity”. In AI 2019: Advances in Artificial Intelligence (Cham, 2019), Springer International Publishing, pp. 344–356