Data Analyst Supply Chain

Varshitha Vulise

Data Analyst

**************@*****.*** +1-940-***-**** Denton, Texas, USA LinkedIn SUMMARY

Results-oriented Data Analyst with over 3 years of experience leveraging Python, SQL, and cloud-based BI tools to deliver data-driven insights for enterprise clients like Dell and VMware. Skilled in data preprocessing, EDA, predictive modeling, and KPI visualization using tools such as Tableau, Power BI, and QuickSight. Adept in Agile methodologies and cross- functional collaboration. Known for translating complex datasets into actionable business strategies that improve customer retention, forecasting accuracy, and operational performance across marketing, supply chain, and product analytics functions.

Key Achivements

•Built churn prediction model at Dell with 87% accuracy using XGBoost and Snowflake.

•Delivered retention insights through Amazon QuickSight dashboards for executive stakeholders.

•Conducted cohort and sales trend analysis to boost marketing and inventory strategies.

•Automated recurring performance reports using Excel Power Query and Macros.

•Led EDA and visualization of customer behavior using Tableau, Seaborn, and PostgreSQL.

•Collaborated in Agile teams using GitHub, JIRA, and Bitbucket to deliver 15+ analytics projects. Skills

Programming & Tools – Python (Pandas, NumPy, Scikit-learn, XGBoost, LightGBM), SQL (PostgreSQL, Snowflake, BigQuery, Redshift), R (Intermediate), Bash/Shell (Basic) Analytics Techniques – EDA, Data Cleaning, Data Mining, Hypothesis Testing, A/B Testing, Regression, Time Series Forecasting (ARIMA, Prophet), Market Basket Analysis, Cohort Analysis, Survival Analysis, Financial Modeling, Scenario Planning, KPI Tracking, Predictive Modeling

Data Visualization & BI – Power BI, Tableau, Looker, Amazon QuickSight, QlikView, Excel Dashboards (PivotTables, Power Query, Macros), Seaborn, Matplotlib, Plotly, Ydata-profiling Cloud Platforms & Data Engineering – Azure (Synapse, Databricks, Data Factory), AWS (S3, Redshift, Lambda), GCP

(BigQuery, Dataflow), Snowflake

Data Pipelines & ETL – Apache Airflow, dbt, Fivetran, Talend, Informatica, Alteryx Version Control & Collaboration – Git, GitHub, Bitbucket, Jira, Confluence, Agile/Scrum, Data Storytelling, Stakeholder Communication

PROFESSIONAL EXPERIENCE

Dell Technologies

Data Analyst

Dec 2021 – Jul 2023 Hyderabad, India

•Performed cohort analysis using Python and SQL on customer transactions to identify retention trends and segment users by engagement lifecycle for targeted marketing strategies.

•Built dynamic dashboards in Looker and Amazon QuickSight, integrating data from cloud warehouses to visualize real- time product performance and sales trends for business stakeholders.

•Developed customized visualizations using Plotly within Jupyter Notebooks and Tableau to highlight supply chain efficiency, shipping delays, and operational gaps.

•Implemented demand forecasting models using scikit-learn and XGBoost to support inventory planning decisions, combining feature engineering and model evaluation techniques.

•Cleaned and preprocessed raw datasets using Python (NumPy, Pandas) and Excel Power Query, handling missing values, normalizing data, and ensuring schema consistency for downstream analytics.

•Tuned model hyperparameters using grid search techniques to enhance forecast accuracy and integrated results into business workflows using Power BI for consumption by non-technical users.

•Participated in Agile-based sprint planning and reviews, managing tasks in JIRA and maintaining documentation in Confluence. Collaborated cross-functionally via Git repositories hosted on Bitbucket.

•Delivered insight-driven reports using storytelling approaches in quarterly reviews, presenting data narratives with visual elements to enhance strategic understanding among leadership teams. VMware

Associate Data Analyst

Jun 2020 – Nov 2021 Mumbai, India

•Conducted exploratory data analysis using Python and PostgreSQL to uncover customer behavior patterns, identify engagement gaps, and support marketing campaign refinement.

•Designed and maintained dashboards in Tableau and Power BI, enabling visualization of key product KPIs, adoption metrics, and usage insights for product and marketing teams.

•Created statistical visualizations using Seaborn and Matplotlib to illustrate user segments, engagement cohorts, and usage frequency for inclusion in internal strategic reports.

•Collaborated on hypothesis testing and A/B testing initiatives by preparing datasets and assisting in statistical evaluations of new feature releases and user interface changes.

•Used Excel tools such as Power Query, PivotTables, and Macros to clean and automate recurring performance reports, ensuring consistency in monthly executive deliverables.

•Contributed to Agile sprint cycles by managing tasks in JIRA, maintaining code in GitHub, and participating in stand-ups and retrospectives to align on analytics deliverables.

•Preprocessed structured customer datasets using Python (Pandas), including handling missing values, reformatting variables, and preparing data for visualization and modeling workflows.

•Presented data-backed insights during weekly team meetings and cross-functional syncs, helping stakeholders interpret behavioral metrics and prioritize optimization strategies. EDUCATION

University of North Texas

Masters in Business Analytics

May 2025 Denton, USA

PROJECTS

Predictive Customer Churn Model – Dell Technologies Developed a predictive churn model for Dell’s enterprise customers using Python (scikit-learn, XGBoost) and SQL

(Snowflake), focusing on behavioral segmentation, feature engineering, and data preprocessing to enhance model accuracy. The project included building explainable outputs through Amazon QuickSight dashboards, allowing business leaders to prioritize at-risk clients and take proactive actions. The solution integrated seamlessly into Dell’s Agile business analytics pipeline and contributed to executive-level KPIs by enabling targeted retention campaigns, ultimately improving customer engagement and lifetime value across key accounts Retail Sales Trend Analysis – Academic Project

Analyzed seasonal retail trends by conducting exploratory data analysis using Python (Pandas, NumPy) and SQL

(PostgreSQL), identifying key purchasing patterns and customer segments. Visualized findings through Tableau dashboards, showcasing product category performance and sales seasonality. The project emphasized data storytelling and KPI alignment, mirroring real-world business analytics practices. It served as a foundation for stakeholder-centric insights by applying segmentation strategies, performance tracking, and trend reporting—skills commonly used in professional data analyst roles to support marketing and operational decision-making. CERTIFICATES

Agile Project Management Foundation Excel Supply Chain Analysis: Solving inventory problems Excel Data Analysis: Forecasting Lean Six Sigma Certifications Certified in Production and Inventory Management (CPIM)

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