Solomon Adeola
Data Scientist
Bangor,Gwynedd
Summary
Knowledgeable Data Scientist with experience delivering in fast-paced, agile environments. Plans and prioritises proactively to achieve project objectives within deadlines. Collaborative team player with excellent problem-solving logic for increased progress towards improvement goals.
Overview
5
5
years of professional experience
7
7
years of post-secondary education
Work history
Research Data Scientist
Bowen University
Iwo, Osun State Nigeria
03.2019 - 07.2022
- Applied analysis skills, leveraging insights, developing and deploying data models and evaluating and improving existing models to create solutions.
- Modelling of Solar Radiation: Developed a predictive model using 6 years of historical solar data (approx. 2,190 data points), achieving an R2 score of 0.92, which successfully optimized energy harvesting strategies and improved solar panel placement by 15%.
- Investigation on Using Available Sunshine Duration Data: Analyzed over 5 years of sunshine duration data from 3 nearby regions, creating models that improved the prediction accuracy of solar radiation estimations by 28% in target locations. This initiative was pivotal in accurately projecting energy yields for upcoming solar installations, mitigating over/underestimations by 33%.
- Temperature Forecasting for Iwo City, Nigeria: Utilized features in ML algorithms to analyze over 100,000 temperature data points from the past decade, achieving a prediction accuracy of 89% and helping local agricultural sectors to increase their crop yields by approx. 20% through optimized planting and harvesting schedules.
Data Science Intern
Solar Energy Research Adv Centre, Bowen University
Iwo, Osun State
10.2017 - 01.2019
- Engineered predictive models using historical solar data, optimizing energy harvesting strategies with a 92% correlation success rate.
- Enhanced solar radiation estimations by analyzing regional sunshine duration data, increasing accuracy by 28% in key areas.
- Leveraged mathematical, statistical, and machine learning techniques to analyze solar energy implications, driving advancements in sustainable agriculture, environmental stewardship, and economic policies within the renewable energy sector.
- Created and presented data visualizations using Matplotlib, effectively communicating complex results to diverse stakeholders.
Education
Master of Science - Advanced Data Science
Bangor University
09.2022 - 09.2023
Master of Science - Physics
Bowen University
Iwo, Osun, State Nigeria 11.2015 - 09.2017
Bachelor of Science - Physics and Solar Energy
Bowen University
Iwo, Osun State, Nigeria 09.2010 - 07.2014
Skills
- Python
- Data Analysis & Interpretation
- Data Visualization & Reporting: PowerBI and Tableau
- Machine learning
- Collaboration
- SQL
- Statistical analysis
- Research and analysis
- Communication skills
Work availability
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
morning
afternoon
evening
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Quote
If you really look closely, most overnight successes took a long time.
Steve Jobs
Timeline
Master of Science - Advanced Data Science
Bangor University
09.2022 - 09.2023
Research Data Scientist
Bowen University
03.2019 - 07.2022
Data Science Intern
Solar Energy Research Adv Centre, Bowen University
10.2017 - 01.2019
Master of Science - Physics
Bowen University
11.2015 - 09.2017
Bachelor of Science - Physics and Solar Energy
Bowen University
09.2010 - 07.2014
Data Projects
1. Predictive Analytics for Customer Lead Conversion
- Conducted exploratory data analysis to identify key predictors of lead conversion, analyzing website engagement, profile completion level, and age; findings informed targeted marketing strategies, capable of resulting in a 25% increase in lead conversion rate.
- Engineered Machine Learning models to predict lead conversion, attaining 84% and 85% accuracy with Decision Tree and Random Forest classifiers, respectively.
- Implemented a customer-based approach to augment phone interactions and performed iterative model refinement, which could lead to a 25% increase in sustained conversion rates and significant revenue growth
2. Predictive Maintenance System for NASA Turbofan Engines
- Designed a system to predict maintenance needs, ensuring measures and reducing downtimes. Available at: https://predmain.streamlit.app.
- The Random Forest Model, attained a root mean square error (rmse) of 47.90 and a correlation score of 0.63 after hyperparameter tuning.
- The Long Short-Term Memory Model achieved a correlation score of 0.70 on the test set and significantly reducing the rmse to 23.11.
3. Recommendation System for Amazon Products
- Created a system to recommend the most suitable Amazon products to users based on past ratings.
- Developed and compared three recommendation models, achieving the best performance with the SVD model: RMSE of 0.8808 and precision of 0.854.
- Ensured continuous model refinement and validated outcomes using varied evaluation metrics (RMSE, precision, recall, F1 score) to ensure robust, relevant, and accurate recommendation delivery.
4. Food-Hub Order Analysis
- Investigated restaurant demand data, identifying that American and Japanese cuisines comprised over 50%of all orders.
- Detected a potential service bottleneck with 10.54% of orders exceeding 60-minute preparation and delivery time.
- Calculated net revenue of $6166.3 with a tailored percentage fee model based on order cost.
- Recommended strategies focused on menu diversification, quality consistency in popular cuisine types, and partnerships with popular establishments for brand enhancement.
Certifications
- Python for Data Science Bootcamp, DataCamp Udemy, 2023
- Applied Machine Learning Using Python, Data Trained Education Pvt Ltd, 2023
- Data Wrangling and Visualisation Using Python, Data Trained Education Pvt Ltd, 2023
- Making Data-Driven Decisions, MIT Institute for Data, Systems and Society, 2023
- Machine Learning Course, Africa Data School Kenya, 2021
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