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Data Science with less than a year in AI/ML, NLP, and Data Visualization tools.
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Highly motivated and results-driven individual with a strong academic background in Data Science and IT. Proven ability to design and implement interactive data dashboards using Tableau, SQL, and Excel for data-driven decision-making. Skilled in developing AI-powered applications for subjective answer evaluation leveraging Google Gemini AI and Natural Language Processing. Eager to apply technical skills in Java, Python, C++, and various data science and machine learning frameworks to real-world challenges.
MIT World Peace University (MIT-WPU)
M Tech · Data Science & Analytics
N/A – June 30, 2026
Ajeenkya D Y Patil University
Bachelor of Technology IT · Data Science
N/A – June 30, 2024
SNBP International School College, Pune.
HSC · Higher Secondary Certificate
N/A – May 31, 2020
Maharashtra State Board :- PDEA
SSC · Secondary School
N/A – May 31, 2018
AI-Based Subjective Answer Evaluation System Using Google Gemini and Natural Language Processing
January 1, 2026 – January 1, 2026
Developed an AI-powered Android application that automatically evaluates subjective answers using Google Gemini AI and Natural Language Processing techniques. The system compares student responses with model answers using semantic analysis and generates scores in real time. Firebase Realtime Database is used for cloud-based storage and management of evaluation results. Designed and developed the complete Android application. Implemented Google Gemini AI API integration for automated answer evaluation. Developed the question bank, answer submission, scoring, and result management modules. Integrated Firebase Authentication and Realtime Database. Performed testing, debugging, and deployment of the application. Successfully automated subjective answer evaluation using Generative AI. Reduced manual assessment effort through real-time AI-based scoring. Implemented semantic evaluation instead of traditional keyword-based checking. Developed a scalable Android application with cloud-based result storage.
Sales Insight Dashboard Using Tableau (SQL, Excel and Tableau)
January 1, 2024 – January 1, 2024
Developed an interactive sales analytics dashboard to track revenue, profit trends, and regional performance for data-driven business decision-making. Designed and built the end-to-end dashboard architecture. Collected, cleaned, and transformed raw sales data using SQL and Excel. Created KPIs, calculated fields, and interactive visualizations in Tableau. Analyzed sales performance by region, product, and time period. Improved sales performance visibility and reporting efficiency. Enabled faster decision-making through dynamic filters and drill-down insights.
Google Data Analytics Professional Certificate
Coursera
June 1, 2026 – Present
Foundations: Data, Data, Everywhere
Google/Coursera
June 1, 2026 – Present
Acquiring Data (Level 1 - AI Fundamentals of AI ASCEND)
Accenture
June 1, 2026 – Present
Oracle Cloud Infrastructure Foundations
Unknown
June 1, 2026 – Present
Python Essentials 1
Unknown
June 1, 2026 – Present
Data Science Associate Program (3 Months)
Pregrad
June 1, 2026 – Present
Internship/Training Certificate
Oasis Infobyte
June 1, 2026 – Present
Foundations: Data, Data, Everywhere
European Open University
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a proactive approach to learning and applying new technologies (e.g., Google Gemini AI, Firebase). The diverse set of certifications and participation in a college hackathon (winner) suggest a driven individual who is eager to learn and contribute. The projects align well with the target role of Data Science, showcasing skills in data analysis, visualization, and AI/ML. The breadth of skills across programming languages, web development, and databases indicates adaptability and a willingness to explore different technical areas, which can be beneficial for cultural fit in dynamic teams.
Soft Skills & Operational Fit
The candidate lists strong soft skills such as communication, leadership, team collaboration, analytical thinking, and problem-solving. These are crucial for operational fit in a data science team, where collaboration and clear communication of insights are paramount. The project descriptions suggest an ability to work through a complete development lifecycle, from design to deployment, which is a positive indicator for operational readiness.