Data Scientist

Designed and implemented the company’s data analysis roadmap and operational OKR, enabling structured performance tracking across branches. Built automated reporting pipelines using Python, SQL, and Google Sheets for daily operational visibility. Analyzed sales, supply chain, and customer data to identify key growth levers and efficiency improvements. Developed predictive sales models and inventory forecasts, improving resource allocation and reducing waste by 20%. Created data-driven decision templates and visualization dashboards to support management meetings and strategic planning. Authored the company’s first IT governance & data security standard, ensuring data consistency and role-based access control. Collaborated with finance and marketing teams to translate data findings into concrete business actions.

Develop and apply machine learning models and AI techniques to solve challenges related to complex AI projects. Participate in data collection, cleaning, and processing to ensure optimal data quality for AI model training and testing. Apply machine learning and deep learning algorithms using Python and frameworks such as TensorFlow or PyTorch for AI model development. Designed and implemented a scalable and efficient AI system architecture to meet business needs. Improved sales prediction model accuracy by 20%, reduced data processing time by 30%, and increased targeted customer segmentation by 25%.

Handled over 100,000 SKUs and monitored product expiration dates, ensuring data accuracy and timely updates. Conducted detailed analysis of sales data, covering a dataset of over 100,000 transactions. Identified sales trends and provided actionable insights to improve business strategies. Implemented processes to track and manage inventory levels, reducing product waste by 15%. Optimized the database for better performance and efficiency, resulting in a 20% increase in data processing speed.

Develop and apply machine learning models and AI techniques to solve challenges related to complex AI projects. Participate in data collection, cleaning, and processing to ensure optimal data quality for AI model training and testing. Apply machine learning and deep learning algorithms using Python and frameworks such as TensorFlow or PyTorch for AI model development. Designed and implemented a scalable and efficient AI system architecture to meet business needs. Improved sales prediction model accuracy by 20%, reduced data processing time by 30%, and increased targeted customer segmentation by 25%.

Created a data warehouse using SQL Server and Excel to efficiently store, organise, and analyse large datasets. Developed processes for data mapping, compliance, and statement validation, ensuring accuracy and reliability of data analyses. Collaborated with cross-disciplinary teams to align campaign strategies with business objectives, resulting in increased efficiency and effectiveness. Reduced data errors by 15%, accelerated data analysis by 40%, and increased marketing campaign success by 10%.