Data Science with 4+ years in Data Analysis, Machine Learning & Power BI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Experienced Data Analyst and Machine Learning enthusiast with a strong academic background in Electronics and Telecommunication Engineering. Possessing 4.0 years of combined internship experience, I excel in conducting exploratory data analysis, building predictive models, and creating interactive dashboards. My expertise spans Python, SQL, Pandas, NumPy, Scikit-learn, and Power BI, enabling me to derive actionable insights and support data-driven decision-making in various projects.
Pimpri Chinchwad College of Engineering
B.Tech · Electronics and Telecommunication Engineering
August 1, 2021 – June 30, 2025
Digidoc Solutions
Project Management Intern
July 1, 2024 – Present
Pune, Maharashtra, India
Turing Techlabs
Operations Intern
January 1, 2022 – December 31, 2023
Pune, Maharashtra, India
Techcare - Intelligent Vitiligo Detection System
April 1, 2025 – June 30, 2025
Analyzed questionnaire-based healthcare data to identify risk patterns associated with vitiligo conditions. Performed preprocessing and feature extraction on structured patient data for predictive analysis. Built classification workflows using machine learning techniques for patient risk categorization and reporting.
View ProjectPower BI Bot Performance Dashboard
January 1, 2025 – March 31, 2025
Designed an interactive dashboard to monitor chatbot performance metrics including response time, resolution rate, and user engagement. Created KPI cards, trend visualizations, and filtering functionalities to improve reporting and operational analysis. Enabled data-driven decision-making by presenting actionable insights through dynamic visual reports.
View ProjectDiwali Sales Analysis and Customer Insights
April 1, 2024 – June 30, 2024
Performed exploratory data analysis on Diwali sales data to identify customer purchasing behavior, product demand, and revenue trends. Applied data cleaning and preprocessing techniques to handle missing values and improve dataset quality for analysis. Analyzed sales patterns based on gender, age group, state, occupation, and product categories to derive business insights. Created visualizations and statistical summaries to support data-driven decision-making and customer targeting strategies.
View ProjectSpace Titanic Survival Prediction and EDA
January 1, 2024 – March 31, 2024
Conducted exploratory data analysis on passenger data to identify patterns influencing survival outcomes. Handled missing values, encoded categorical variables, and performed feature engineering for predictive modeling. Built and evaluated machine learning classification models using Scikit-learn to predict passenger transport outcomes.
View ProjectMachine Learning for Engineering and Science Applications
NPTEL
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a breadth of application areas, from healthcare to sales analysis and chatbot performance, indicating adaptability and a willingness to explore diverse problem domains. The target role is Data Science, and the projects align well with core data science tasks. The candidate is still pursuing a B.Tech degree, which suggests a learning-oriented mindset. The internships, while not deeply technical, show exposure to professional environments. The candidate's achievements, such as qualifying GATE DA and participating in hackathons, suggest a competitive and driven individual, which generally aligns well with a performance-oriented culture.
Soft Skills & Operational Fit
The candidate's internship experiences, though not directly technical in data science, indicate an ability to work in cross-functional teams, handle data, and support operational analysis and project coordination. This suggests a foundational understanding of business processes and collaboration, which are valuable soft skills for a data science role. However, the descriptions are brief and do not provide deep insights into problem-solving or independent initiative.