Developed survey questions using LLMs, tailored to diverse demographic groups to optimize survey effectiveness.Leveraged LLMs and prompt engineering to conduct market research tasks, including identifying consumer trends, validating participant selection criteria, crafting personalized survey invitations, etc.Designed and deployed an interactive chatbot using ManyChat for survey distribution, significantly increasing respondent engagement and boosting the response rate to 50%.Developed a comprehensive data analysis strategy using LLM, analyzed qualitative and quantitative survey data, and generated reports that contributed to key business insights and recommendations.Developed proficiency in Python and various data science libraries (like Pandas and NumPy) to perform comprehensive sentiment analysis on consumer reviews.Designed and validated an AI-driven presentation plan, and created a polished presentation using Gamma.app and Google Slides, incorporating custom charts and visuals to effectively communicate data-driven insights to stakeholders.Built an interactive dashboard using Claude Sonnet, enabling dynamic data visualization and making complex insights accessible to stakeholders, which facilitated informed decision-making.

To enhance student learning in Data Analysis and Regression, I took an active role in providing instructional support, which was instrumental in guiding over 60 students towards achieving academic excellence. My approach was multifaceted, involving not just traditional teaching methods, but also incorporating hands-on activities, real-world examples, and personalized mentorship. This immersive and supportive environment was pivotal in helping students grasp complex concepts and apply them effectively in various scenarios.In addition to direct instruction, I placed a significant emphasis on providing constructive feedback on assignments. This involved a detailed analysis of each student's work, highlighting strengths and pinpointing areas for improvement. My feedback was tailored to encourage a growth mindset, enabling students to view challenges as opportunities for learning rather than setbacks. This approach contributed to a noticeable 15% improvement in the class average grades, underscoring the effectiveness of targeted feedback in enhancing learning outcomes.The positive changes in student performance were not just limited to improved grades; there was also a palpable shift in the classroom dynamics. Students became more engaged, participatory, and willing to take intellectual risks. The culture of continuous improvement and open dialogue fostered a collaborative learning environment, where students felt supported in their academic journey. My commitment to active instructional support and constructive feedback played a crucial role in creating a conducive learning atmosphere that promoted not only academic success but also personal growth and development.

As a Store Planner, or Retail Space Planner, from September 2022 to May 2023, I played a pivotal role in spearheading design projects that significantly enhanced the retail environment. My approach was data-driven, with a strong focus on leveraging customer experience insights to inform our designs. This strategic emphasis on understanding and catering to customer needs and preferences led to a remarkable 20% boost in sales, highlighting the direct impact of thoughtful design on business performance.Leading a dynamic team of five, I was responsible for steering project management efforts to ensure that key initiatives were not only conceived with innovation in mind but also executed efficiently and effectively. My leadership facilitated a collaborative and productive team environment, where each member was empowered to contribute their best. This collective effort was instrumental in meeting deadlines and achieving our project goals, thereby maintaining the momentum of our strategic retail enhancements.One of the hallmarks of our success during this period was the implementation of innovative space optimization strategies. By reimagining the use of retail space, we were able to make significant improvements in how products were displayed and interacted with by customers. This initiative led to a 15% increase in product visibility, which in turn enhanced customer satisfaction. The effectiveness of these strategies was evidenced by positive outcomes in post-project surveys and feedback, indicating a clear alignment between our spatial enhancements and customer expectations. This period of my career was marked by a blend of creative problem-solving, team leadership, and a keen focus on delivering tangible results that aligned closely with both business objectives and customer satisfaction.

• Led development of predictive models for bridge deterioration and maintenance scheduling, leveraging PySpark and Pandas to enhance data processing and model accuracy.• Conducted thorough data analysis to identify trends in bridge performance, informing strategic decision-making and stakeholder communication.• Collaborated with engineering teams to integrate advanced neural network architectures, achieving a significant reduction in mean squared error for predictive maintenance models.• Implemented MLOps practices to monitor model performance and drift, utilizing Tableau dashboards for comprehensive reporting.• Explored and applied various NLP methods, including cosine similarity and K-means clustering, to categorize 85% of maintenance data into 5 unique clusters, enhancing data organization and insight extraction.

• Developed and evaluated predictive models for structural simulations of high-rise buildings using PySpark and Pandas, enhancing design and construction processes.• Implemented advanced neural network architectures to optimize building stability predictions under environmental factors such as wind and seismic activity, reducing error rates and improving safety.• Created machine learning pipelines and tuned hyperparameters using Hyperopt, significantly reducing mean squared error for stability predictions.• Conducted data analysis and applied NLP techniques, including cosine similarity and K-means clustering, to categorize construction data and improve project planning and decision-making.• Collaborated with architects and engineers to ensure compliance with building codes and standards, utilizing data-driven insights to enhance overall project outcomes

- Conducted data analysis on Indian soil databases to explore soil properties and their correlation with agro-climatic zones.- Spearheaded data cleaning processes to ensure accuracy and reliability of soil datasets.- Employed statistical techniques and regression modeling to uncover relationships between soil characteristics and geographical locations.- Utilized Geographic Information System (GIS) mapping to visualize and interpret spatial patterns of soil attributes.- Identified key factors influencing soil properties within different agro-climatic zones through regression analysis.- Provided valuable insights for agricultural planning and land management strategies based on soil data analysis.- Collaborated with cross-functional teams to communicate findings and implications of soil data analysis.- Contributed to the advancement of knowledge in soil science and agricultural research through data-driven approaches.

• Contributed to the development of a predictive model aimed at forecasting the response rate of marketing campaigns to minimize costs. Engaged in feature engineering and hyperparameter optimization, achieving an accuracy of 87%.• Conducted data analysis and generated insights on inflation rates, bottlenecks, churn rates, and various KPIs using data visualization tools such as Tableau and Power BI.• Extracted insights and created customer reports at multiple levels using Tableau and SQL, employing both inferential and descriptive statistical methods.