Raj Purohith Arjun

Data Science Intern (Graduate) - Texas A&M University, College Station

• Analyzed 1Lakh+ soil CO2,N2O and CH4 flux measurements using automated sensors and trace gas analyzers,ensuring 95% precision in environmental data collection.
• Developed and optimized ETL pipelines for high-frequency climate and agricultural sensor data using Apache Airflow, Python, and PySpark, reducing data preparation time by 30% and improving data reliability for precision farming applications.
• Built scalable data infrastructure supporting multi-terabyte satellite and IoT sensor data from 40+ interconnected applications, enabling real-time monitoring of soil health, crop conditions, and climate patterns at an ingestion rate of 50Hz.
• Designed efficient database solutions and optimized SQL queries, enhancing data analysis speed by 20% and enabling real-time insights for agriculture yield forecasting and climate risk assessment.
• Developed 10+ Tableau dashboards for farmers, agronomists, and policymakers to facilitate data-driven decision-making.
• Applied advanced ensemble modeling techniques (Bagging, Random Forest, Gradient Boosting, AdaBoost) to improve crop yield prediction accuracy and optimize resource allocation in sustainable farming practices.

• Designed fine-grained attribute grounding to enhance the accuracy and explainability of vision-language models, improving performance by 20%.
• Optimized machine learning algorithms for visual-textual data integration, increasing LLM efficiency by 25%.
• Collaborated with cross-functional teams to refine machine learning algorithms for text classification, demonstrating teamwork and expertise in AI-driven solutions.

• Led the development and optimization of AI-powered consumer research solutions using advanced machine learning models and techniques, driving significant improvements in customer insights and engagement.
• Designed and implemented machine learning models to analyze and predict consumer behavior using advanced AI techniques, including Emotion AI, Behavior AI, and Predictive AI, resulting in more accurate consumer insights.
• Utilized Python, Pandas, and NumPy to manage large-scale consumer data sets, processing millions of data points to uncover actionable insights and enhance model performance.
• Applied supervised learning techniques, including logistic regression and support vector machines (SVM), to predict customer behavior and tailor personalized marketing strategies, improving campaign effectiveness by 25%.
• Enhanced predictive accuracy through data preprocessing techniques such as normalization, scaling, and outlier detection, ensuring more reliable results in consumer behavior forecasting.
• Developed and fine-tuned recommendation systems for personalized consumer experiences, leveraging collaborative filtering and content-based methods to improve user engagement and satisfaction by 20%.
• Employed advanced data visualization tools like Matplotlib and Seaborn to present complex consumer behavior patterns and trends, helping stakeholders easily interpret data and make informed decisions.
• Led the implementation of AI-powered insights into consumer preferences and behaviors, driving 30% more accurate product recommendations and improving marketing ROI.
• Utilized SQL and cloud platforms (AWS, GCP) to streamline data pipelines and optimize data storage and retrieval processes, reducing data processing times by 40%.
• Collaborated with cross-functional teams, including product managers and marketing analysts, to ensure AI models aligned with business goals and consumer expectations.
• Delivered technical presentations to both technical and non-technical stakeholders, effectively communicating the impact of AI-driven consumer insights and providing actionable recommendations for improving consumer engagement.

• Built and deployed a comprehensive AI-enabled Fintech B2B cloud application, focusing on creating a scalable, full-stack web-based product.
• Leveraged Python libraries like Pandas, NumPy, and Scikit-learn for in-depth data analysis, as well as JavaScript for interactive data visualization.
• Conducted extensive data preprocessing and feature engineering, including data cleaning, wrangling, normalization, and scaling, to prepare datasets for predictive modeling.
• Employed text vectorization techniques to classify user segments for targeted financial services.
• Led the data analysis and machine learning aspects, implementing classification models such as Gradient Boosting, XGBoost, and Random Forest with extensive parameter tuning.
• Evaluated model performance using metrics like accuracy, precision, recall, and F1-score to select the most effective model for financial risk prediction.
• Utilized advanced anomaly detection techniques to identify payment discrepancies, increasing fraud detection capabilities by up to 90%.
• Conducted A/B testing to analyze transaction patterns and collaborated with marketing teams, leading to targeted campaigns that improved transaction rates.
• Executed SQL operations, including complex joins and data aggregation, to streamline the ETL process, enhancing data transformation and storage in a centralized database.
• Set up new database schemas and optimized queries to improve data management and retrieval speed.
• Developed automated data transformation workflows, converting raw data into formats suitable for analysis. Created an experimental framework for automated data collection and built real-time approval systems, reducing manual intervention and increasing productivity by 30%.
• Collaborated with cross-functional teams, including UI/UX designers and backend developers, integrating machine learning insights into the application to enhance user experience.
• Visualized data insights using tools like Tableau and R Markdown, providing detailed exploratory analysis and uncovering key business trends.
• Identified key areas for procedural improvement through customer data analysis, providing actionable insights that enhanced decision-making and profitability.
• Applied various clustering techniques to detect underperforming segments, leading to strategic adjustments that boosted overall system efficiency.
• Maintained a high standard of attention to detail in handling data and building models, ensuring the accuracy and reliability of predictions. Effectively communicated insights through written reports and presentations to stakeholders, facilitating data-driven business decisions.

• Selected as a judge for Student Research Week (SRW) 2025 at Texas A&M University, providing a platform for students across disciplines to showcase their innovative work through oral and poster presentations.
• 2025 Appointed as a Judge and Mentor for the 2-day TAMU Hack 2025 hackathon, guiding participants, providing technical mentorship, and evaluating 20+ projects based on innovation, execution, and impact.
• 2025 Selected as a DataAnnotator- GENA(Task-Based)for snorkel.ai, where I annotated and refined data to enhance AI model's performance in mathematical problem-solving tasks.
• 2024 Won Best Paper Award at International Conference on Computing Technologies for Sustainable Development-2024 for "Innovation in Vehicle Tracking : Harnessing YOLOV8 and Deep Learning Tools for Automatic Number Plate Detection" Check
• 2023 Won Best Paper Award at the National Conference on Technology for the Society’23 for the research paper “ Enhancing ANPR using YOLOv8 and Deep Learning Techniques” held at SRMIST, Chennai Check
• 2023 Received an Academic Award for Overall Proficiency Rank-1 in the Computer Science Department for the Year 2023 and 2024 , SRM University Check
• 2023 Ranked in the top 10 out of 2500 participants in Proglint’s Alliance University Computer Vision Hackathon 2023.