Data Analyst with less than a year in statistical modeling & data 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
Aspiring Data Analyst pursuing M.Sc. in Statistics with strong skills in Python, R, SQL, Excel, and statistical modeling. Experienced in data cleaning, visualization, forecasting, and analytical problem-solving through academic projects and industrial training. Passionate about transforming raw data into actionable insights to support business decision-making.
Kavayitri Bahinabai Chaudhari North Maharashtra University, Jalgaon
Master of Science · Statistics
August 1, 2024 – June 30, 2026
Moolji Jaitha College, Jalgaon
Bachelor of Science · Statistics
August 1, 2021 – June 30, 2024
Sardar G. G. Highschool and Junior College, Raver
12thth (HSC)
N/A – May 31, 2021
Dr. N. N. Akole Madhymik Vidhylaya, Raver
10th (SSC)
N/A – May 31, 2019
Jain Irrigation Systems Limited
On-Job Training – Jain Irrigation Systems Limited, Jalgaon
March 1, 2026 – June 1, 2026
Jalgaon, Maharashtra, India
Process Capability Analysis on RR Caliper
June 1, 2024 – Present
Performed process capability analysis on automotive brake caliper dimensions using SPC tools (X-R, I-MR charts) and Cp/Cpk indices. Verified process stability and normality using Anderson-Darling and Kolmogorov-Smirnov tests, concluding a highly capable manufacturing process.
Statistical Analysis of Import and Export of Crude Oil & Petroleum Products in India
June 1, 2024 – Present
Utilized Minitab and MS Excel for data analysis, visualization, and forecasting to derive insights on energy security and trade policy. Conducted statistical and time-series analysis of India's crude oil and petroleum imports (1998-2025), applying time-series plots and exponential smoothing for trend evaluation and demand forecasting.
Dynamic Regression Modelling of Financial Markets: A Comparative Study of DLM, ARDL and SSM Models
June 1, 2024 – Present
Developed and compared Distributed Lag Models (DLM), Autoregressive Distributed Lag (ARDL) and State Space Models (SSM) for modeling NIFTY 50 financial returns. Performed time-series analysis, forecasting, cointegration testing, and model diagnostics using R programming. Evaluated model performance using RMSE, MAE, MAPE, AIC and BIC for forecasting accuracy and model comparison.
Distributed Lag Models (DLM) PG Level Seminar
June 1, 2024 – Present
Presented theoretical foundations and practical applications of Distributed Lag Models in econometrics and time-series analysis. Covered lag structures, finite DLM estimation using OLS, assumptions, limitations of static regression and macroeconomic applications. Explained short-run and long-run effects, lag interpretation and dynamic modeling concepts using practical examples.
Python Programming in Hindi
Great Learning
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in diverse applications of statistics, from manufacturing quality control to financial markets and economic analysis. This breadth of interest suggests adaptability and a willingness to tackle different types of data challenges. The academic focus aligns well with roles that require rigorous analytical thinking and problem-solving. The internship at Jain Irrigation Systems Limited shows an inclination towards practical application in an industrial context, which is a positive indicator for a data analyst role in a business setting. The extracurricular activity (short film competition) suggests a well-rounded individual, potentially bringing creativity to problem-solving.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to conduct detailed analytical work and present findings (e.g., DLM seminar). The internship experience suggests an ability to apply theoretical knowledge in a practical, industrial setting, working within a quality control department. The focus on reducing rejection rates and improving efficiency points to a results-oriented approach. However, without direct interview data, assessing collaboration, stress handling, or specific communication styles beyond written project descriptions is difficult.