Prasad Pawar

Background

About Prasad Pawar

As an experienced Data Scientist with over three years of expertise, I have consistently delivered impactful solutions that empower teams across various business functions. With a solid foundation in Python, SQL, Machine Learning, Natural Language Processing, and Deep Learning, I thrive in transforming complex data into actionable insights. Key Achievements: Product Recommendations: Spearheaded the development of a product substitute recommender system for sales teams, utilizing advanced techniques like regex, NLP, and Whoosh indexing to achieve highly accurate product matching. This innovation has significantly improved decision-making efficiency. Time Series Forecasting: Co-developed a versatile forecasting tool integrating over six models, ranging from traditional statistical methods to state-of-the-art algorithms. Implemented a rolling window cross-validation strategy to ensure optimal accuracy for finance, inventory, and sales forecasting. GPT-based Solutions: Created a comprehensive HR solution leveraging GPT technology, streamlining document retrieval and query responses. This tool has enhanced HR productivity by providing dynamic and insightful information access. Inventory Management: Developed a machine learning system to improve cycle count accuracy, increasing hit rates significantly by identifying high-risk items and locations through predictive analytics. Machine Learning and NLP Projects: Delivered innovative solutions including quality complaints forecasting, email classification, customer churn prediction, product attribute extraction from PDFs, and AI-driven product description generation for e-commerce platforms. Professional Journey: Current Role: As a Data Scientist, I lead and contribute to projects that enhance operational efficiency and data-driven decision-making across multiple domains. Internship Experience: Demonstrated expertise as a Data Scientist Intern, where I led a project to predict flight prices with exceptional accuracy, achieving notable improvements in the test environment. Technical Proficiency: Languages: Proficient in Python and SQL. Technologies: Experienced with ML, NLP, DL, Azure, and Docker. Libraries: Skilled in utilizing libraries such as Sklearn, Pandas, NumPy, Matplotlib, NLTK, Gensim, Spacy, TensorFlow, Keras, Langchain, and OpenAI.