Neha Vashist | Data Analyst at Deloitte

Neha Vashist

Data Analyst at Deloitte

Based in Hyderabad, India

About

Experienced Data Analyst with expertise in Python programming, SQL, and Tableau, coupled with experience in cloud technologies such as Microsoft Azure, and data processing tools like Databricks and PySpark. Demonstrated proficiency in a wide range of machine learning algorithms, including linear regression, logistic regression, decision tree, k-means, etc. Possessing knowledge in statistics, probability, and linear algebra. Skilled in exploratory data analysis and creating interactive dashboards. Possessing foundational knowledge in Sentiment Analysis using Natural Language Processing. Proven ability to collaborate with cross-functional teams and leverage data for informed decision-making. Detail-oriented with excellent communication skills.

Skills

Projects

Target Brazil E-commerce Insights

This project analyzes a dataset from Target's Brazil operations, focusing on 100,000 orders (2016-2018). It explores customer demographics, order processing, pricing, payment, freight, and satisfaction to provide actionable insights.

Netflix Content Analysis

This project analyzes Netflix's extensive dataset, encompassing 10,000+ movies and TV shows. The goal is to provide data-driven insights for content production and business growth globally. Recommendations will focus on understanding content availability, trends in releases, optimal launch times etc

AeroFit Treadmill Customer Profiling

Through descriptive analytics and contingency tables, I have uncovered insights into customer characteristics affecting product preferences. The analysis includes outlier detection, feature impact assessments, probability computations, and correlation exploration to provide actionable recommendation

Walmart Customer Purchase Analysis

The project assesses spending habits, detects outliers, computes confidence intervals, and provides insights on average expenses. Recommendations based on findings aim to guide business decisions.

Yulu Electric Cycle Demand Analysis

This project aims to identify factors affecting demand for shared electric cycles in the Indian market. Utilizing a dataset with variables like weather, season, and working day, the analysis involves exploratory data analysis, hypothesis testing (2-sample T-Test, ANOVA, Chi-square).

Delhivery Data Intelligence

This project involves cleaning, aggregating, and extracting features from raw data. Techniques include groupby aggregations, handling missing values, feature engineering, and hypothesis testing. The goal is to provide actionable insights to improve efficiency, quality, and profitability in logistics

Jamboree GradAdmit Analysis

Analyzing factors like GRE, TOEFL scores, SOP, LOR Strength, GPA, & Research Experience, we'll use Linear Regression, Ridge, & Lasso models. EDA, VIF, residuals analysis, & performance metrics guide our evaluation. Uncover interdependencies, test assumptions, & offer actionable insights

LoanTap Credit Analysis

Leveraging Logistic Regression and precision-recall tradeoffs, we conduct EDA, handle missing values, outliers, and engineer features. Evaluating ROC AUC, precision-recall curve, and classification report, we address the delicate balance of minimizing defaults while maximizing opportunities.

Ola Driver Churn Analysis

With strategic feature engineering, class imbalance treatment, and model evaluation, the solution provides actionable insights to tackle high driver churn. ROC AUC, Classification Report, and targeted algorithms enhance decision-making in driver retention strategies.

Experience

Jan'23-Present

Data Analyst at Deloitte

In my current role at Deloitte, I play a pivotal role in enhancing learning initiatives and optimizing course content for an enriched user experience. I implement streamlined data processing using Microsoft Azure pipelines, Databricks, and PySpark, reducing manual efforts and ensuring data accuracy. Through strategic timing initiatives and Tableau dashboards, I've contributed to a 7% increase in loss coverage, resulting in substantial cost savings. My involvement extends to developing and deploying advanced models, such as sentiment analysis using MultinomialNB, and conducting A/B tests with hypothesis testing. Leveraging optimization algorithms, I identify optimal course deployment methods, leading to an 8% boost in learner engagement.

Aug'21-Jan'23

Business Analyst at Etech Global Services

In my role at Etech, I spearhead the development and deployment of a robust text analytics model for sentiment analysis and topic identification. This initiative has led to a significant 20% increase in positive user interactions and provided valuable insights into user sentiments. Utilizing topic modeling techniques, I uncover actionable insights for content optimization, resulting in a commendable 15% boost in user engagement. Additionally, I create interactive dashboards using Medallia software to provide stakeholders with real-time data for informed decision-making, while also ensuring the seamless flow of data from multiple sources to the Medallia platform.

Education

2017-2021

Computer Science and Engineering from Lovely Professional University

CGPA: 8.96