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Data Analyst with less than a year in Data Analytics & Business Intelligence
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M.Sc. Statistics graduate with demonstrated experience in data analytics, business intelligence, and dashboard development. Proficient in Python, R, SQL, Power BI, and Tableau, with hands-on expertise building end-to-end data pipelines, performing data validation and cleansing, and translating complex requirements into actionable insights and metrics reporting. Skilled in exploratory data analysis, predictive and prescriptive analytics, and statistical modelling. Currently expanding into Large Language Models and Generative AI, with growing familiarity with LLM concepts including fine-tuning and RAG. Strong communicator with a proven ability to present data-driven findings to both technical and non-technical audiences.
Savitribai Phule Pune University (SPPU)
M.Sc. Statistics · Machine Learning, Statistical Foundations of Data Science, Time Series Analysis, Bayesian Inference, Design & Analysis of Clinical Trials, Stochastic Processes, Regression Analysis
August 1, 2023 – August 1, 2025
Abasaheb Garware College
B.Sc. Statistics · Foundation in statistical theory, probability, and computational methods
August 1, 2020 – August 1, 2023
Deloitte Australia - Forage Program
Data Analytics Associate
January 1, 2024 – June 1, 2024
India
JPMorgan Chase & Co. - Forage Program
Quantitative Research Analyst
January 1, 2024 – June 1, 2024
India
Assessing Quality of Life & Burden Amongst Parents of Children With ASD, ADHD, CP & Typically Developing Children
June 1, 2026 – Present
Designed and executed a complete data operations pipeline: survey design, primary data collection of 30+ responses, validation, cleansing, inferential statistical analysis and statistical modelling across 4 groups (ASD, CP, ADHD, and Typically Developing (TD) groups). Conducted literature reviews across peer-reviewed studies to guide study design and validate methodologies. Designed structured questionnaires for caregivers, achieving 95% response reliability. Applied inferential methods (t-tests, chi-square, ANOVA, factor analysis) in RStudio to identify stress factors; revealed caregiver burden 30% higher in neurological groups. Maintained reproducible R scripts improving efficiency by 25%.
Data Analysis of Indian Unicorn Startups
June 1, 2026 – Present
Collected and processed a dataset of 70+ unicorn startups across 10+ cities, performing exploratory data analysis to uncover patterns and opportunities for growth. Built an end-to-end analytical pipeline - raw data ingestion through to interactive Power BI dashboards demonstrating proficiency in data pipeline construction and metrics reporting. Collaborated on requirement definition and translated business questions into data-driven visualizations, supporting decision-making.
Nashville Housing Data Analysis
June 1, 2026 – Present
Performed end-to-end analysis of 56,372 property transactions in the Nashville metropolitan area (2013 - 2016), covering the full data science pipeline from raw SQL cleaning through predictive modelling and unsupervised clustering. Conducted multi-stage data cleaning in SQL Server (date standardization, address splitting, duplicate removal) and Python (normality testing, group-wise median/mode imputation, Winsorization), reducing missing data from 54% to 0%. Executed six formal hypothesis tests (Kruskal-Wallis, Mann-Whitney U) with effect sizes to confirm that location, land value, building quality, property age, and vacancy status are statistically significant price drivers. Built and compared six regression models (OLS, Lasso, Ridge, Random Forest, XGBoost, LightGBM); XGBoost achieved the best performance (Test R2 = 0.457, RMSE = 0.560 on log-price), a 32% improvement over OLS baseline. Engineered 8 features including log transformations, property age/age-squared (confirming a U-shaped price curve with minimum at ~47 years), price-per-acre, and sale-to-assessed ratio (median 1.28 across the dataset). Segmented the residential market via K-Means clustering (k=2, silhouette = 0.530): a Mid-Market segment (90.6%, avg. $197K) and a Luxury/Premium segment (9.4%, avg. $496K), validated with PCA visualization.
View ProjectData Analytics Virtual Program
Deloitte Australia - Forage
June 1, 2026 – Present
Statistical Analysis and Research using Excel: Advanced Excel for data analysis and reporting
Unknown
June 1, 2026 – Present
Quantitative Research Program
JPMorgan Chase & Co. - Forage
June 1, 2026 – Present
Business Analyst Program: Data-driven decision making, business analytics
Finlatics
June 1, 2026 – Present
Complete Python Programmer from Scratch to Applications
Udemy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects showcase a diverse range of applications, from healthcare (ASD, ADHD, CP study) to finance (JPMorgan internship, Nashville Housing) and business (Indian Unicorn Startups). This breadth indicates adaptability and an interest in applying data analysis across different domains. The virtual internships with Deloitte and JPMorgan Chase & Co. suggest an initiative to gain practical experience and align with corporate environments. However, the experience is primarily academic and virtual, which might require some adjustment to a full-time corporate culture.
Soft Skills & Operational Fit
The candidate demonstrates strong soft skills including written and verbal communication, attention to detail, ability to work independently and collaboratively, time management, technical documentation, and stakeholder presentation. These skills are crucial for a Data Analyst role, indicating a good operational fit for roles requiring both analytical rigor and effective communication.