I am a Business Analyst with a strong track record of helping companies optimize operations, scale efficiently, and drive sustainable growth across multiple industries. My expertise combines critical thinking, strategic planning, and advanced data analytics to transform raw data into actionable insights that fuel business success.
Throughout my career, I have successfully scaled startups from small operations into multi-million-pound organizations, leveraging data-driven decision-making to enhance efficiency, reduce costs, and maximize profitability. By integrating predictive analytics, automation, and process optimization, I enable businesses to streamline operations and unlock new growth opportunities.
With experience in logistics, technology, accounting, and other industries, I understand the unique challenges that different sectors face. My ability to bridge the gap between business strategy and data science ensures that companies not only make smarter decisions but also achieve measurable, long-term success.
I am passionate about empowering businesses to harness the full potential of their data, driving efficiency, innovation, and competitive advantage in an ever-evolving marketplace. Let's turn your data into a powerful strategic asset.
Throughout my career, I’ve consistently leveraged an analytical mindset to drive operational efficiency and business success. I began honing my skills in the logistics industry, where I developed and monitored key performance indicators (KPIs) using tools like Excel to optimise delivery times and reduce fuel consumption.
Over time, my analytical expertise has evolved, expanding into data analytics and data science through continuous learning and practical application across diverse business domains. This journey has enabled me to apply predictive analysis, data visualisation, and process optimisation to solve complex problems, enhance decision-making, and deliver measurable improvements.
This MySQL-based project explores global layoff trends through a combination of data cleaning and exploratory data analysis (EDA). The dataset underwent rigorous preprocessing to handle inconsistencies, missing values, and ensure data integrity.
Technologies Used: MuSQL Workbench., Tableau
In May 2024, more than 65,000 developers participated in Stack Overflow's annual survey, which explored various topics, including coding practices, the technologies and tools developers use, their aspirations for learning, AI, and their overall work experience.
Technologies Used: Python: Jupyter lab , Tableau
Clean data is the foundation of accuracy and reliability when it comes to gleaning insights from analysis, leading to better decision-making by eliminating errors, inconsistencies, and biases. For this project, I want to demonstrate an understanding of the processes and technologies required to take raw data and transform it into clean, usable data.
Technologies Used: Python: Jupyter lab
One of the largest retail chains in the world wants to use its vast data source to build an efficient forecasting model to predict the sales for each SKU in its portfolio at its 76 different stores using historical sales data for the past 3 years on a week-on-week basis. Sales and promotional information is also available each week, both product and store-wise.
Technologies : Python, Jupyter Lab, Scikit-Learn
Road freight is not universally renowned for its investment and move towards cutting edge technologies. Whilst some firms are beginning to understand and invest in areas such as predictive analytics, AI, and machine learning. Many are still over invested in time- consuming, labour-intensive practices that measure historic trends and week old KPI’s.
Feel free to reach out to us for any inquiries or to schedule a consultation. I am here to help with your data analysis, business intelligence, and data visualization needs.
Lee Wilson
Hull, United Kingdom
