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Data Science with less than a year in Data Analysis & Machine Learning.
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Data Science Intern with 7 months of experience optimizing business reports and developing real-time dashboards to accelerate decision-making and improve data reliability. Skilled in Python, SQL, Machine Learning, and data visualization, with a proven track record in building predictive models and recommendation systems. Currently pursuing a Bachelor of Science in Data Science, bringing strong analytical and technical skills to drive data-backed strategies.
SIES COLLEGE OF SCIENCE ARTS AND COMMERCE
Bachelor of Science · Data Science
August 1, 2022 – June 30, 2025
NoBrokerHood
Data Science Intern
May 1, 2025 – November 1, 2025
India
Customer Churn Prediction
November 1, 2024 – December 1, 2024
• Designed an artificial neural network model with 85% prediction accuracy using a dataset of 10,000+ customer records, enabling accurate churn analysis. • Developed automated cleaning workflows with Pandas and NumPy, improving preparation speed by 40% and reducing repetitive manual steps by around 30% through more efficient process structuring. • Deployed a real-time churn prediction app on Streamlit, adopted by 50+ users, and aligned deployment with existing infrastructure. Incorporated user feedback to enhance usability by about 25%. • Provided stakeholders with detailed churn-driver reports and clear presentations, enabling three data-backed strategies and a projected 15% improvement in retention.
Content-Based Movie Recommendation System
July 1, 2024 – September 1, 2024
• Built a movie recommendation app using Python and Streamlit, delivering personalized results with a 4.5/5 satisfaction rating and achieving strong early adoption from 50+ initial users. • Cleaned and processed a 5,000-movie dataset using Pandas, ensuring consistent data quality and resolving key data-wrangling challenges that improved model accuracy by around 15%. • Developed a content-based recommendation algorithm in Scikit-learn using genre, overview, and cast features, generating relevant suggestions with high match accuracy and reducing irrelevant recommendations by about 20%. • Designed a SQL database to store user profiles and history for 50 users, improving recommendation retrieval time by 20% through query optimization and model fine-tuning.
DBMS Course
Scaler
June 1, 2026 – Present
Business Analytics With Excel
Simplilearn
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects and internship experience align well with a Data Science role, demonstrating a clear interest and foundational skill set. The diversity of projects (recommendation systems, churn prediction, business reporting) shows a broad application of data science principles. The use of various tools like Python, SQL, Pandas, NumPy, Scikit-learn, Streamlit, and Power BI indicates a good breadth of technical skills relevant to the field. The candidate's proactive approach to learning, as shown by certifications, suggests a growth mindset.
Soft Skills & Operational Fit
The candidate demonstrates a results-oriented approach, evidenced by quantifiable achievements in projects and internships. Collaboration with the PIA team and providing stakeholder reports indicate good teamwork and communication skills. The ability to incorporate user feedback suggests adaptability and a user-centric mindset. The candidate's experience in optimizing processes and reducing manual effort aligns well with operational efficiency.