Rijul Singh Malik

Spearheaded a transformation initiative, training a team of 20 on Agile methodologies and tools, resulting in a 15% increase in project delivery speed and a 20% reduction in defects. Enhanced operational efficiency by integrating Spark scripts for real-time analytics; optimized ETL processes using Hadoop, PySpark resulting in a 30% increase in revenue. Built an end-to-end user chatbot using LLMs such as GPT integrating RAG for user relevant and intent from knowledge base, further testing the model using Precision, Recall and F1 Score.

Implemented CI/CD pipelines to streamline model training and deployment processes, reducing deployment time by 40% and enabling seamless scaling of machine learning applications across platforms. Spearheaded the development of a personalized NLP system based using NLTK, SpaCy; improved customer engagement and boosted repeat purchases by 25%.

Developed and implemented a robust Time Series pricing strategy based on SVM, Naïve Bayes; optimized pricing structures, which led to a 20% boost in profit margins within six months. Developed a comprehensive product pricing analysis using Tableau increasing profitability by 30% through strategic pricing adjustments; refined pricing model based on customer feedback and market trends. Engineered end-to-end E-Commerce solution, integrating Decision Trees, Random Forest for predictive analytics, resulting in 40% faster data processing and reduced storage costs by 25%.

Led a team in conducting comprehensive correlation testing across various customer touchpoints, optimizing pricing strategies and enhancing user experience using confidence intervals; the initiative led to a 40% reduction in customer churn rate. Led a cross-functional team in the migration of data from DBT to Postgres, ensuring data integrity and availability for BI visualization; project completed 2 months ahead of schedule and reduced data retrieval time by 50%.

Developed and executed innovative learning programs for team members on media analytics and modeling techniques; improved team proficiency in Causal and Bayesian Inference, leading to enhanced campaign performance. Optimized data warehouse performance by fine-tuning queries and indexing strategies using TensorFlow resulting in a 40% reduction in query execution time and improved overall system efficiency.

Implemented pricing optimization algorithms using NumPy, Pandas and Scikit Learn to analyze market trends and competitor data, resulting in a 15% increase in profit margins and a more competitive pricing strategy that aligned with consumer preferences. Implemented an intermediate stack leveraging JIRA for project management and Git for version control to streamline supply chain processes, resulting in a 25% reduction in deployment errors.