Oluwabori Ige

Oluwabori IGE Machine Learning Engineer • Linkedin • • - • portfolio GitHub • - 02, FUTA Southgate, Akure. Ondo State. Nigeria PROFESSIONAL SUMMARY Highly experienced and passionate Senior Machine Learning/Data Engineer with over 2 years of real-world ML-focused development experience. Formal training in Computer Science and Software Engineering. Proficient in designing, building, and maintaining large-scale distributed machine learning technologies, including recommendation systems and NLP. Strong expertise in Python, SQL, and data analysis tools/libraries. Skilled in data visualization and ML techniques using Scikit-learn and TensorFlow. Excellent communicator and team player with a data-driven and experimental approach to decision-making. TECHNICAL SKILLS Programming Languages Python, SQL, Excel, VBA Data Analysis Tools/Libraries Pandas, Numpy, Matplotlib, Seaborn, Spider, Requests, Selenium Data Visualization Tools Tableau, MS Excel, PowerBI ML Libraries /Techniques Sckit-learn, Tensorflow, PyTourch Other Tools MS Office, (PowerPoint, Work) Soft Skills Report writing, Teamwork, Critical Thinking, Communication, Accuracy, Independent, Growth, English Hard Skills Clients Needs, Data Ingestion, Data Wrangling, Machine learning, Statistical Analysis, Management, Modelling, Queries, Team members, Testing, Data Warehouse, Git, Technical Capability, Performance Enhancement, Amazon Web Services Management. FEATURED PROJECTES Title: Data Exploration on Road Anomaly Classification To learn more about the patterns and traits of road abnormalities, I conducted a thorough exploratory analysis on the dataset. Examining descriptive statistics, spotting trends, and using data visualization tools were all part of this investigation. Important information regarding the prevalence and severity of road anomalies was gleaned through the exploratory analysis. Additionally, the Folium map's data visualization indicates the areas in need of immediate attention, assisting in resource allocation and decision-making. GitHub Repository Full Report Title: Deep Neural Network Models Training on Road Anomaly Dataset The Road Anomaly collection contains cases of roads with cracks, potholes, and bumps that have been gathered from various locations. To ensure the Road Anomaly dataset's quality and suitability for training the deep learning models, I carried out extensive data preprocessing on it. I created a deep learning model specifically for finding anomalies on roads. The pre-processed dataset was used to train the deep learning model through model development on patterns and attributes related to road anomalies. I measured the model's effectiveness using a variety of measures. The integration with ArcGIS also offers a user-friendly interface for viewing and analysing the outcomes, promoting the ability to make well-informed decisions. GitHub Repository Full Report Python Data Cleaning Pandas Seaborn MS Excel Folium Python Data Cleaning Pandas Seaborn ArcGis TensorFlow Title: Predicting the new Basketball Hall of fame Title: BlackCopper Web Scraping and Analysis The project utilized linear regression techniques and web scraping to explore the factors influencing the induction of players into the Hall of Fame. This project aimed to uncover patterns and predictive insights to identify future potential Hall of Fame candidates. The project entail performing web scraping on a list of URLs, followed by comprehensive data cleaning and analysis on the gathered data. The project aimed to extract valuable information through data wrangling from websites and derive actionable insights through meticulous data processing and analysis. GitHub Repository Full Report GitHub Repository Full Report Python Data Cleaning Pandas Seaborn MS Excel Plotly Python Data Cleaning Pandas Seaborn MS Excel Spider RELEVANT EXPERIENCE June 2023 – present Data Engineer, Routebolt Limited, Akure • Developed and maintained pipelines for ingesting and processing real-time road sensor data. Also, engineered features from raw sensor data, ensuring quality and consistency. • Designed and managed scalable databases for efficient storage of road condition data. • • Integrated machine learning models into data pipelines and production systems. Collaborated cross-functionally with data scientists and mobile app developers connecting them through developed APIs for exposing road condition data, ensuring security and compliance. • June 2021 – present Optimized data processing and storage systems for scalability and performance on Amazon Web Services. Research Assistant, Smart Energy Research laboratory, Akure • Researched on evaluation of the state of metering of Electricity in Nigeria and collected data through literature reviews while accurately cataloguing citation information. • Developed an API for data exchange between IoT devices and servers using Django REST framework. • Explored research findings through statistical analysis to build an innovative method using a mix of inertia sensors to detect road anomalies. • Develop python script to automate data collecting from IOT devices linked via API. • Enhanced machine learning algorithm that trains on data on road anomalies through scalability and optimization. • Managed administrative tasks such as organizing research data, maintaining laboratory equipment. Held costs 5% under budget. June 2021 – present Data Science Intern, Smart Energy Research laboratory, Akure • Through the hardware development stages, I coordinated a project team to carry out research, development, and implementation for a federal government smart meter project. • Build and test algorithms and mathematical models to improve data extraction and synthesis saving 4 hours. • Refining existing data road vibration data extraction process, saving 4 hours increasing technical capabilities. July 2016 – Mathematical Instructor, Mathematical Department, Sharon Rose College December 2016 • Coordinating up to 70 students at once to guarantee proper involvement and allocating assessments and assessments to ascertain students' progress. • Created detailed syllabus to outline teaching methods, learning objectives to prove successful working within tight deadlines and fast-paced atmosphere. • Used student feedback to make continuous improvements to the curriculum and teaching techniques. • Addressed systematic issues with math curriculum by collaborating with colleagues. Also, administered assessments and standardized tests to evaluate student progress. EDUCATION 2016 - 2023 Bachelor of Engineering, Computer Engineering Federal University of Technology Akure, Ondo State, Nigeria. [GPA: 4.47/5.0] PROFESSIONAL CERTIFICATIONS AND WORKSHOP Certifications 2023 Applied Data Science Lab, WorldQuant University 2022 Advance Learning Algorithms, DeepLearning.AI (via coursera). Supervised Machine Learning: Regression and Classification, DeepLearning.AI (via coursera) 2021 Introduction to IoT, Cisco Networking Academy. Introduction to Cybersecurity, Cisco Networking Academy Introduction to Packet Tracer, Cisco Networking Academy Programming Essential in Python, Cisco Networking Academy. Workshop 2023 Imagine 2023: The Conference for edge AI. 2023 Computer Engineering Department, FUTA: Academic Research Methodology 2018 School of Engineering and Engineering Technology, FUTA: The Role of Engineer in National Development 2017 Annual Conference of School of Engineering and Engineering Technology, FUTA: Innovation and Adaptive Technology for National Development. HONORS AND AWARDS MTN Scholastic Foundation Awards, FUTA 2019 The scholarship was a yearly payment of 200,000 Naira given to indigent and brilliant undergraduates to support their studies. Dean’s List/Faculty Honor List, FUTA (7 in 10 semester) - Merit award by School of Engineering and Engineering Technology to Outstanding student in the department for a particular academic semester PROFESSIONAL AFFILIATIONS • Data Science Network (DSN) ID - DSN/AIPlus/2020/13748 • Kaggle Community • Zindi Community • Programing Essentials in Python (CISCO)