Amrithanshu Kesoju

About Amrithanshu Kesoju

Driven by curiosity and a passion for data-driven innovation, I am a Master’s graduate in Computer Science from Illinois Institute of Technology, deeply interested in Data Science, and Advanced Analytics. I thrive on solving complex problems, uncovering hidden patterns in data, and optimizing decision-making through statistical modeling and AI-driven insights. A quick learner with a expanding foundation in computational thinking, algorithmic optimization, and scalable system design, I enjoy translating raw data into impactful narratives that drive business and technological advancements. My technical expertise spans Python, R, SQL, and big data frameworks (Apache Spark, Hadoop) for large-scale data processing. I have hands-on experience in containerized deployments (Docker, Kubernetes), cloud computing (AWS, DynamoDB, Lambda, API Gateway), and CI/CD automation, ensuring seamless integration of machine learning pipelines. My passion for predictive analytics and time-series forecasting led me to explore ARIMA, Prophet, and attention-based transformers, fine-tuning hyperparameters using Bayesian Optimization for precision forecasting. During my tenure as a Junior Data Analyst Intern at JVR Systems, I applied advanced machine learning algorithms to optimize business intelligence strategies, implementing feature engineering techniques, automated ETL pipelines, and anomaly detection models. I spearheaded a VR motion analysis project, leveraging unsupervised learning (DBSCAN, HDBSCAN) and sequence modeling to infer behavioral patterns. My Used-Car Dynamic Pricing Model combined multi-linear regression with heteroskedasticity adjustments, refining price elasticity predictions using principal component analysis (PCA) and time-series decomposition. Additionally, I architected a Library Data Management System with a polyglot database approach (MongoDB, DynamoDB, PostgreSQL), optimizing query performance and data consistency.

Experience

1. Junior Data Analyst

   Jvr Systems Inc

   Jan 2025 — Present · TX, US

   Extracted, cleaned, and transformed large-scale Retail Sales data from multiple sources using SQL and Python and libraries like Pandas and Sckit-Learn. Developed Tableau dashboards for real-time monitoring of sales trends, demand fluctuations, and inventory levels. Applied time series forecasting (ARIMA and Exponential Smoothing) to predict future sales, improving forecast accuracy by 25%. Automated data pipelines and reporting using Python scripts and SQL, reducing manual effort by 40%.

Education

• Illinois Institute of Technology

  Master of Science - MS, Computer Science

• Sikkim Manipal Institute of Technology

  Bachelor of Technology - BTech, Computer Science

  2019 — 2023