• Analyzed requirements and developed comprehensive project plans to guide data science initiatives

• Strategized and implemented detailed work plans to ensure project milestones and deadlines were met

• Designed and developed an innovative academic enhancement product using LangChain and LLM, improving teaching and correcting workflows

• Collaborated effectively within a team using Agile methodologies, resulting in on-time project delivery that met all objectives
• Built and executed high-performance data pipelines to process massive datasets exceeding 10B rows. Employed big data technologies, such as Spark and SQL in Databricks, to source and compile grocery Ad data for forecasting and analysis
• Engineered highly accurate predictive models, predicting ad effectiveness for three distinct product categories across two banners. Attained R2 score of 0.85, enabling compelling storytelling and influencing business decisions
• Streamlined dashboard data retrieval by refining SQL queries, slashing their quantity by over 60%, resulting in a significant reduction in cloud resource consumption and markedly enhancing overall processing speed
• Crafted an interactive dashboard that visually presents diverse customer metrics, unveiling spending patterns across customer types, thereby facilitating in-depth analysis of purchase behavior and empowering strategic marketing/inventory management
• Interacted with various stakeholders to ensure proper utilization of new product, which directly helped their reporting capabilities and accuracies

•Developed predictive models using Random Forest, XGBoost, and Gradient Boosting, improving product demand forecasting accuracy by 18%, optimizing inventory management, and reducing stockouts by 15% through the analysis of transactional data, customer demographics, and product reviews.
•Engineered new features such as time-based shopping patterns, customer health profiles, and regional trends, leading to the successful implementation of personalized health supplement recommendations and increasing average order value by 20%.
•Built and deployed a churn prediction model using Logistic Regression and SVM, analyzing customer behaviour to reduce churn by 12%, and integrated NLP techniques for sentiment analysis on unstructured data, improving customer satisfaction insights by 30%.
•Leveraged TensorFlow and Keras to automate nutritional product image classification through deep learning, reducing manual processing time by 40%, and integrated real-time IoT device data (fitness trackers) to enhance personalized health recommendations, increasing customer engagement by 22%.
•Led A/B testing and statistical analysis using Python and R on marketing campaigns, identifying key drivers of customer retention, which resulted in a 15% improvement, while designing Tableau