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Data Science with 1+ years in Data Analysis, Machine Learning, and Statistical Modeling
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Assessing your cultural and operational fit
Motivated MSc Statistics student with knowledge of data analysis, machine learning, and statistical modeling. Skilled in R, Python, SQL, Tableau, and Power BI with interest in data-driven problem solving and business analytics.
Kavayitri Bahinabai Chaudhari, North Maharashtra University, Jalgaon
M.Sc. · Statistics
August 1, 2024 – June 30, 2026
Z.B. Patil College, Dhule
B.Sc. · Statistics
August 1, 2021 – June 30, 2024
Gram Vikas Vidyalaya Junior College, Pimpalgaon Hareshware
12th (HSC)
N/A – May 31, 2021
District Statistical Office (DSO), Dhule
ON JOB TRAINING
May 1, 2025 – June 1, 2025
Dhule, Maharashtra, India
Performance Evaluation of Machine Learning and Deep Learning Models on Real Dataset
January 1, 2026 – December 31, 2026
Implemented Machine Learning and Deep Learning models on real-world datasets. Developed models using Python, Scikit-learn, TensorFlow, and Keras. Performed data preprocessing, feature engineering, and visualization. Worked with ANN, MLP, RNN, LSTM, Random Forest, and XGBoost models. Evaluated model performance using Accuracy, RMSE, MAE, F1-Score, and R2 metrics.
Study of Urbanization and Its Socio-Economic Impacts in India
January 1, 2025 – December 31, 2025
Studied how urbanization in India has changed over time using census and government data. Analysed urban and rural population trends and calculated demographic indicators such as growth rate, birth rate, and death rate. Examined the relationship between urbanization and socio-economic indicators like literacy, income, birth rate, and infant mortality using correlation analysis. Assessed inequality in urban population distribution across states using the Lorenz Curve and Gini Coefficient.
A SURVEY ON MOBILE BANKING AND ITS IMPACT ON CUSTOMER SATISFACTION
January 1, 2024 – December 31, 2024
Designed and administered structured questionnaires to collect both primary and secondary data. Applied statistical tools (sample size determination, proportion tests, chi-square tests, and randomness tests) for data analysis and interpretation. Presented findings on customer satisfaction trends, usage patterns, and key factors influencing mobile banking adoption.
Python Course
Kaggle
May 1, 2026 – Present
Introduction to Tableau Public Software
Unknown
January 1, 2026 – Present
Excel full course
Infosys Springboard
July 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying statistical and machine learning techniques to diverse real-world problems, including customer satisfaction, urbanization, and general model performance evaluation. This aligns well with a data science role that often requires tackling varied challenges. The breadth of tools and models used (Python, R, SQL, Tableau, Power BI, various ML/DL models) indicates a willingness to learn and adapt, which is a positive cultural fit for dynamic technical environments. The internship at a government office also shows an ability to work within established processes and contribute to data-driven planning.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work with real-world datasets, apply statistical methods, and present findings, suggesting a methodical and analytical approach. The internship experience points to an understanding of data management and reporting in a structured environment. However, without direct assessment data on communication, logical reasoning, or teamwork, it is difficult to fully assess soft skills and operational fit beyond what is implied by project completion.