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Data Science with 2+ years in Machine Learning & Statistical Analysis
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Skilled in statistical analysis, machine learning, and deep learning with strong programming expertise in R, Python, SQL, and Excel. Experienced in data cleaning, exploratory data analysis, and building predictive models to generate actionable insights. Passionate about solving real-world problems using data-driven approaches. Holds an M.Sc. in Statistics with a strong foundation in statistical theory and analytics.
Savitribai Phule Pune University (SPPU)
Master of Science · Statistics
September 1, 2022 – May 1, 2024
Savitribai Phule Pune University (SPPU)
Bachelor of Science · Statistics
June 1, 2019 – May 1, 2022
Flat Price Prediction
May 1, 2024 – May 1, 2024
Scraped and cleaned Gurugram real estate data from 99acres; merged flats and houses datasets. Performed feature engineering, EDA, outlier handling, and mean/median imputation. Employed linear regression, decision trees, XGBoost, and random forest algorithms for prediction. Achieved an accuracy of 90% with the random forest model.
Indian Premier League (IPL) Power BI Dashboard
May 1, 2024 – May 1, 2024
Designed an interactive dashboard using Power BI to analyze IPL data from 2008-2024, focusing on team performance, player stats, and venue insights. Utilized Power Query for data cleaning and transformation, and DAX for creating KPIs like win ratio, average runs, and wickets per match. Created dynamic visuals showing matches per stadium, toss impact, and team rankings across all seasons. Integrated slicers and filters for real-time exploration of teams, venues, and seasons.
SQL (Basic) and SQL (Intermediate)
HackerRank Certification
June 1, 2026 – Present
Python (Basic)
HackerRank Certification
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic background in Statistics and project work align well with a Data Science role, demonstrating a foundational understanding of the field. The projects show initiative in applying learned concepts to real-world (academic) scenarios. The breadth of skills listed (ML, DL, MLOps, databases) suggests a willingness to learn and adapt. However, the lack of professional experience means cultural fit in a corporate environment is largely unproven.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work independently on data-driven problems, from data acquisition and cleaning to model building and visualization. The academic projects suggest a structured approach to problem-solving. However, without direct work experience or psychometric test results, it is difficult to assess collaboration, stress handling, or communication clarity in a professional setting.