Vipul Dhamde
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Data Analytics, Data Engineering,Data Science
Warsaw, Masovian Voivodeship, Poland
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Advisor - Customer ServiceCapitaOct 2025 - Current (1 year 1 month)• Delivered exceptional customer service by handling inbound calls and general enquiries for Bord Gáis Energy customers,
ensuring timely and accurate resolution of account, billing, and service-related queries.
• Supported sales and retention initiatives by identifying customer needs, promoting energy plans and value-added services,
and contributing to customer satisfaction and loyalty goals.
• Utilized Salesforce Engage and Oracle CCB systems to manage customer interactions, document case details, and maintain
data accuracy, while meeting key performance indicators (KPIs) related to response time, quality, and compliance standards.- TData Analyst InternTechvieioFeb 2025 - Aug 2025 (7 months)• Collaborated directly with clients to gather product requirements and align business objectives, while conducting in-depth competitor and market analysis to identify opportunities and industry benchmarks.
• Performed end-to-end EDA on competitor product data to uncover customer behavior, pricing trends, and feature demand; designed and fine-tuned ML models using hyperparameter tuning to enhance accuracy and guide data-driven market entry and product strategy.
• Translated complex data into clear, impactful insights using dynamic Power BI dashboards; effectively communicated findings to stakeholders, supporting strategic decision-making and cross-functional collaboration. - Data AnalystHotelKeyMay 2024 - Aug 2024 (4 months)Resolved 50+ critical issues across 4,000+ Hilton properties, improving operational reliability on the Property Engagement Platform.
Automated backend tasks using scripts, solving 85% of reported issues with high efficiency.
Collaborated using Agile methodology with cross-functional teams to deliver fast-paced, data-driven solutions.
Tools: Jenkins, Jira, SQL, Python - Ml internWhizoid StudioMay 2023 - Nov 2023 (7 months)Optimized preprocessing on image datasets, increasing classification model accuracy by 20%.
Automated data scraping with Beautiful Soup & Selenium, achieving 95% accuracy.
Developed statistical models for image classification in sparse high-dimensional environments.
Tools: Python, Pandas, Sklearn, Selenium - AWSF TechnologiesSep 2022 - Dec 2022 (4 months)Worked on designing secure network infrastructure using Amazon VPC.
Gained hands-on experience with AWS Databases and cloud services - GResearch InternGHRCE CoE AI MLDec 2021 - Jan 2023 (1 year 2 months)Conducted sentiment analysis on 50,000+ eCommerce reviews with 85% model accuracy.
Built exploratory and predictive models to analyze customer trust metrics.
Co-authored an IEEE CIS conference paper on trust prediction from product reviews. - InternAZeotropy IIT BombayMay 2021 - Jun 2021 (2 months)Na
Education
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- Master of Science - MS, Data Science and analyticsUniversity College CorkJan 2024 - Dec 2025 (2 years)Focused on Generalised linear modeling,Machine Learning, Deep Learning, Data Mining, Agile Methodologies Tools/Technologies: Python, R, SQL, Azure, AWS
- Bachelor of Technology - BTech, Data ScienceGH Raisoni College of EngineeringGHRCE NagpurJan 2020 - Dec 2024 (5 years)
Projects (professional or personal)
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- AAutomating the Machine Learning Process using PyCaret and StreamlitMay 2023 - May 2026 (3 years 1 month)Na
Publications
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- IAutomating the Machine Learning Process using PyCaret and StreamlitIEEE INOCON BangloreApr 2023Machine learning applications for the industry have seen significant growth and attention in recent years. As a result, there is a significant need for Machine learning engineers across the business, but increasing their productivity is still a major problem. For time-consuming Machine learning pipeline operations such as data pre-processing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis, Automated Machine Learning (AutoML) has arisen as a solution. In this research, we examine the condition of the AutoML application, which aims to automate ML operations. We do multiple evaluations based on numerous datasets, in various data segments, to assess their functionality and compare the outcomes.
- Automating Machine Learning Process using PyCaret and StreamlitIEEEMar 2023In this Publication I presented a streamlined approach of automating machine learning workflows by integrating PyCaret, a low-code machine learning library, with Streamlit, an open-source app framework. my work demonstrates how this combination facilitates rapid development of interactive ML applications, enabling users to perform tasks such as data preprocessing, model training, and evaluation with minimal coding. This integration aims to make machine learning more accessible to non-experts and accelerate the deployment of ML solutions in various domains.
- E-commerce Product’s Trust Prediction Based on Customer ReviewsIEEEJul 2022This publication which is presented at the Third Congress on Intelligent Systems (CIS 2022) and published in the Lecture Notes in Networks and Systems series by Springer. In this study, I developed a novel, reproducible data processing methodology to analyze customer reviews across 10 product categories from one of India’s leading e-commerce platforms, encompassing 11,559 reviews. Leveraging various machine learning algorithms, I aimed to automatically classify product reviews as highly trustable or not-so-trustable, achieving up to 85% accuracy. My findings have significant implications for understanding consumer trust, potentially aiding in reducing product returns and enhancing customer satisfaction.