PROMISE EZEKIEL
Email:-/ Phone: -
USA
PROFILE
Motivated Data Science graduate student with a strong foundation in machine learning, data analysis, and
statistical modeling. Passionate about leveraging data-driven insights to solve real-world business challenges.
Experienced in working on data science projects involving predictive analytics, feature engineering, and
data visualization, with hands-on experience in Python, SQL, and machine learning libraries such as
TensorFlow and Scikit-learn.
EDUCATION
River State University, Nigeria
Master of Science in Computer Science (2023)
Bachelor of Science in Computer Science (2019)
SKILLS
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Programming Languages: Python, R, SQL, Java
Data Science & Machine Learning: Supervised & Unsupervised Learning, Deep Learning, NLP, Data
Mining, Statistical Analysis
Data Tools & Platforms: Hadoop, Spark, Cloud Platforms (Azure, Heroku), Power BI, Tableau
Libraries & Frameworks: Scikit-learn, TensorFlow, PyTorch, Numpy, Pandas, Matplotlib, SciPy
Big Data & AI: Feature Engineering, Data Pipeline Development, Predictive Analytics
Soft Skills: Problem-Solving, Team Collaboration, Communication
EXPERIENCE
Graduate Researcher (Data Science & Machine Learning)
Towson University | 2025 – Present
• Conducted research in machine learning applications for IoT intrusion detection, utilizing statistical
inference and deep learning techniques.
• Built predictive models for anomaly detection using Python, TensorFlow, and Scikit-learn.
• Designed and optimized big data processing pipelines using Spark and cloud computing resources.
• Analyzed large datasets and performed feature engineering to improve model performance.
SENIOR DATA SCIENTIST
ITWave Limited, UK
January 2022 – 2024
• Developed machine learning models for customer segmentation and behavior prediction, improving
business decision-making.
• Designed and maintained data pipelines, ensuring efficient data preprocessing and feature extraction.
• Applied NLP techniques to extract insights from unstructured text data, enhancing sentiment analysis
models.
• Collaborated with senior data scientists and business analysts to translate analytical insights into actionable
recommendations.
Senior Data Scientist
Trontier, Port Harcourt
September 2017 to 2021
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Led a team of data scientists to develop machine learning solutions focused on improving customer
retention and increasing sales performance.
Utilized NLP techniques to analyze customer feedback, providing valuable insights for product
improvement and customer satisfaction.
Implemented large-scale data processing systems using Hadoop and Spark, delivering high-performance
analytical solutions.
Data Analyst
Redefined Tech Hub, Port Harcourt
Internship (May 2016 to Sep 2021)
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Conducted exploratory data analysis and statistical modeling for business intelligence reporting.
Designed visualization dashboards using Tableau and Power BI, providing key business insights.
Built predictive models for sales forecasting, reducing inventory costs by 15%.
Projects
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IoT Intrusion Detection System – Developed a hybrid model for intrusion detection using machine
learning techniques, implemented data preprocessing, feature engineering, and model optimization to
improve accuracy, conducted A/B testing to validate the system’s performance against traditional
security measures.
Big Data Predictive Analytics – Built a data pipeline using Spark and Hadoop to process large-scale
datasets, applied machine learning algorithms to predict customer churn and improve retention
strategies.
Fraud Detection System – Designed a machine learning model using anomaly detection techniques to
identify fraudulent transactions, optimized feature selection to improve fraud detection rates,
developed real-time alert systems to enhance security measures.
Sentiment Analysis for Product Reviews – Utilized NLP techniques to analyze customer feedback
from online product reviews, built a classification model to determine sentiment polarity, created
interactive dashboards for sentiment trend visualization.
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Stock Price Prediction using LSTM – Developed a deep learning model using LSTM networks to
predict stock price movements, implemented time-series feature engineering and hyperparameter
tuning for better forecasting accuracy.
GitHub Portfolio: https://github.com/tochile?tab=repositories
LinkedIn: https://www.linkedin.com/in/promise-ezekiel/
Google Scholar: https://scholar.google.com/citations?user=7XETibQAAAAJ&hl=en
Certifications
AWS Machine Learning Foundations (Udacity)
REFERENCES
Available upon request
