Ajisafe Michael
About Me
I am a data driven professional with a passion for discovering hidden insights in complex data sets and driving impactful business results. I am highly skilled in data analysis and visualization tools such as Microsoft Excel, R, Python, SQL, Power BI, and Google Sheets, along with building machine learning models using Tensor Flow, Scikit-Learn, and Keras. My work spans across data storytelling, exploratory data analysis, predictive modeling, and end-to-end machine learning systems.
As a data enthusiast, I enjoy working on a diverse range of projects, from market research to user behavior data, and I like to bring my creative approach to the table in every project. Whether its cleansing data sets or crafting unique data visualizations, my goal is to make sense of the data and turn them into actionable insights that drive business growth.
Apart from my technical expertise, I enjoy collaborating with people from diverse backgrounds to gain different perspectives and find solutions rooted in data. When I’m not helping organizations harness the power of data, I’m usually sharing my knowledge and insights on the internet or working on cool data visualization projects.
I’m proud of my track record of delivering innovative data-driven solutions and I’m always looking for the next challenge to help businesses succeed. If you’re looking for a passionate and experienced data scientist to join your team, feel free to reach out to me!
Education
Federal University Oye Ekiti, Nigeria
Work & Experience
Data Scientist intern
Hamoye AI
• Analyzed ecological footprint data for 50+ countries, employed machine learning techniques to predict quality metrics with 95% accuracy, resulting in a 20% increase in informed decision-making and policy planning. • Implemented convolutional neural networks (CNNs) for image recognition, achieving a 98% accuracy rate on a dataset of 10,000 images, boosting productivity by 25% and gaining widespread industry recognition. • Cleansed, scrutinized, and synthesized data on a dataset of 20 million rows and 50 files, resulting in a 40% increase in data usability and actionable insights for businesses via intuitive visualizations. • Devised a powerful multivariate multiple regression model with an R-squared value of 0.85, probed the impact of eight input variables on two output variables (heating and cooling loads) for residential buildings, that resulted in savings of 30% in utility costs and rejuvenated the ideal blend of comfort and sustainability for residents.
Freelance Data Analyst
Upwork
• Processed and sanitized disparate data sources using various programming languages like Python and R, increasing the total usable data by 50%. • Built various statistical and machine learning models utilizing SQL and Hive, resulting in a 25% improvement in model accuracy and 30% increase in automated data pipeline efficiencies. • Utilized predictive modeling techniques such as customer segmentation, purchase forecasting, and customer churn prediction to drive a return on investment (ROI) of 30% for the clients. • Developed compelling data visualizations utilizing Matplotlib, Seaborn, and Plotly libraries, leading to an increase in data-informed decision-making by 40%. • Extensively experimented using A/B testing, forecasting, and deep learning, optimizing the overall performance of the client's data ecosystem by 40%. • Presented complex findings to non-technical stakeholders utilizing a communication-first mindset, resulting in better decisions, accelerated decision-making, and gaining deep trust in the data-driven decision-making process.
HP Tech Ventures Extern
HP
• Conducted in-depth market research by analyzing start-up metrics, industry data, team profiles, and business models to determine product differentiation and strategy for 5 startup sectors. • Developed comprehensive market maps for each of the 5 startup sectors, identifying key players and trends. • Utilized data cleaning methods and tools like Open Refine to manage and organize large volumes of data, resulting in improved data quality and accuracy. • Utilized Excel and SQL to calculate summary values and generate visual representations of data, providing a comprehensive understanding of the data and its implications. • Presented and correlated data using data visualization techniques, recommending attractive start-ups for further investment due diligence based on a thorough analysis of their potential for growth and profitability.
