this is my resume : Rahul Kotian
linkedin.com/in/rahulkotian26 | rahulkotian26@gmail.com | rahulkotian98.github.io/portfolio1 | 680.216.3807 Education
Syracuse University School of Information Studies
Master of Science in Information Systems
Mumbai University
Bachelor of Engineering in Computer Engineering
May 2023
GPA: 3.67/4.0
October 2020
GPA: 3.3/4.0
Courses: Introduction to Data Science, Project Management, Information System Analysis, Cloud Management, Strategic Management, Enterprise Risk Management, Lean Six Sigma, Big Data and Analytics, Artificial Intelligence & Machine Learning, Applied Mathematics, Data Warehousing and Mining, Database Management Systems
Skills
Data Science: Machine Learning, Predictive Modeling, Data Mining, Topic Modelling, Sentiment Analysis, Supervised/ Unsupervised Learning, and Libraries (NumPy, Pandas, Scikit-learn, PySpark, Tensorflow, NLTK, Matplotlib, Plotly) Technologies: SQL, Python, R, Kaizen, Selenium, Bloomberg, Jira, ANOVA, Matlab, Git/GitHub
Business Analytics: Excel, Microsoft Power BI, Tableau, SSAS, Report Designer BI, Alteryx, SAS
Cloud Technologies: Amazon Web Services, Microsoft Azure, Google Cloud Platform Experience
Data Analyst | Dynamic Sustainability Lab July 2022 - May 2023
• Conducted comprehensive analysis on more than 150 energy measurement points within a $2.11 billion enterprise utilizing Power BI, Report Designer BI, and EnergyCAP.
• Initiated the creation of an advanced Power BI dashboard, integrating diverse data sources, conducting Data Mining and Data Wrangling, crafting custom visualizations, and providing ongoing updates to senior leadership.
• Orchestrated the implementation of an innovative energy resource tracking system leveraging Power BI and SQL Server Analysis Services (SSAS), with a projected 15% reduction in energy usage.
Projects
Healthcare Data Analysis | Introduction to Data Science December 2022
• Spearheaded a team of 5 to conduct Exploratory Data Analysis (EDA) and streamline data using R, unveiling critical drivers of escalating healthcare expenses.
• Implemented a robust machine learning model integrating Linear, K-Support Vector Machines (KSVM), and RTree models, achieving an accuracy of 75% and sensitivity of 80% in projecting high-cost patients for the upcoming year.
• Employed R to craft insightful visualizations including maps, box plots, histograms, bar charts, and pie charts, culminating in identification of four actionable insights aimed at curbing individual healthcare expenditures.
Next Word Prediction | Natural Language Processing August 2019
• Advised the development and implementation of cutting-edge NLP strategies, resulting in a 25% increase in accuracy for next-word prediction tasks.
• Directed the management and processing of extensive datasets, comprising over 23,000 unique words, showcasing expertise in handling large-scale data, leading to a 30% improvement in model robustness.
• Developed a streamlined LSTM model with 128 neurons, achieving a remarkable 95% accuracy rate in language prediction, surpassing industry standards by 20%.
UX Research November 2019
• Influenced product development at Google Generative AI, Meta, Universal Music Group, Spotify, Google Maps, Google Pay, YouTube, YouTube Shorts, Suntory, and TransPerfect through UX research initiatives.
• Shaped over 10 prototypes with key insights, impacting product design and innovation.
• Recommended 50+ video conferences to drive data-driven improvements for 15+ products; identified customer pain
points through analytics, boosting customer satisfaction by 25% and retention rates by 20%.
Spoken Language Understanding using Pretraining Technique | SSRN Research Publication October 2020
• Achieved a 15% performance enhancement for the SLU model by conducting intricate dataset analysis, refining preprocessing techniques, and optimizing feature selection for NLP tasks.
• Resolved data-related challenges to bolster model robustness, resulting in a 20% increase in implementation accuracy and ensuring the delivery of reliable insights.
• Engineered a streamlined speech recognition model that achieved an exceptional accuracy rate of 95% using pre-trained machine learning. Managed a dataset comprising 248 distinct phrases across 31 intents totaling 10 GB in size.