Kiran Ranganalli
Data Enthusiast
ABOUT ME
Hey there, Welcome!
I'm Kiran, a data analyst with 3+ years of experience turning messy data into clean, actionable insights. I've worn multiple hats—Analytics, Business Intelligence, Data engineering, and ETL wizardry—and I hold a Master’s in Business Analytics (a fancy way of saying I love data a lot).
I’ve worked across insurance, e-commerce, and fintech, helping teams make smarter, faster, and more informed decisions. My toolkit includes:
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SQL, Python, Power BI, Tableau, dbt
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AWS (S3, Glue, Redshift, Athena, SageMaker)
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Apache Airflow, Spark, Snowflake, Hive, GCP, Azure
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And a dash of R, Scikit-Learn, XGBoost, and TensorFlow when the machine learning mood hits.
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Curious to See the Data in Action?
If you're the kind of person who likes seeing proof in the project pudding—
let’s jump into some of my favorite data projects and the impact they made.
Scroll down if you're more into the behind-the-scenes academic and work experience stuff—I’ve got plenty of real-world stories to share too.
Projects
Built an end-to-end NLP pipeline using AWS (S3, Athena, SageMaker) and OpenAI GPT to auto-generate 100K+ business responses from 40GB of Yelp reviews.
Applied prompt engineering, semantic search, and RAG architecture to improve chatbot context and speed.
Analyzed patient flow, staff allocation, and equipment usage to identify bottlenecks and inefficiencies across hospital operations.
Used R and Python to model trends and simulate resource planning scenarios, improving decision-making.
Designed an interactive Power BI dashboard to analyze sales trends, top-selling items, and revenue performance across outlets.
Used DAX and Power Query to automate KPIs like daily revenue, peak order times, and customer favorites.
Analyzed 30K+ data points from CoinMarketCap API to uncover trends in market cap, volatility, liquidity, and price behavior.
Applied clustering and correlation techniques to detect stablecoins, top performers, and outliers in crypto trends.
Used Python, Pandas, Seaborn, and Scikit-Learn to visualize insights and build predictive models for price movement.
Analyzed 84,000+ transactions using PCA-transformed features to identify patterns linked to fraudulent behavior.
Built and evaluated models like Logistic Regression, Random Forest, and XGBoost with SMOTE to handle class imbalance.
Analyzed customer data to reveal that 2% of users drove 90% of revenue, enabling hyper-targeted marketing.
Built Power BI dashboards and conducted A/B tests that boosted retention by 17% and cut reporting time by 40%.
Optimized user funnels and ad campaigns using Amplitude and GA.
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Master of Science in Business Analytics
San Francisco State University | Jan 2023 – Dec 2024
GPA: 3.7/4.0
What I tackled: Data Mining, Predictive Analytics, Business Intelligence, Machine Learning, Advanced Statistics... basically all the fun stuff that makes spreadsheets come alive.
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Bachelor of Technology (B.Tech)
VNR Vignana Jyothi Institute of Technology | July 2016 – Dec 2020
GPA: 8.2/10.0
Where the spark began. This is where I learned to love solving real-world problems with logic, code, and just enough caffeine.
Professional Experience (3+ Years)
Delivered Korea
Data Analyst Intern
San Francisco, California.
2024
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This e-commerce internship was full of “wow” moments.
I found out that just 2% of users drove 90% of revenue—which reshaped how we did targeting and budgeting. -
I created real-time Power BI dashboards that made GTM meetings actually exciting (and 40% faster).
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Ran A/B tests on loyalty campaigns that boosted repeat purchases by 17%, and ad campaign tweaks that increased ROI by 18%.
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Oh, and I spotted a few UX bottlenecks using Amplitude + Google Analytics that, once fixed, increased conversions by 12%.
Capgemini
Data Analyst
2020-2023
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This is where I got deep into the insurance world—and into the data trenches.
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I built ETL pipelines that handled 150GB+ of claims data daily using AWS Glue + Apache Airflow (yep, it was intense).
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I helped cut false payouts by 12% using SQL magic in Redshift and made fraud patterns easier to catch.
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Also designed secure, self-serve dashboards in Power BI with row-level security—so everyone saw just what they needed (nothing more, nothing less).
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Plus, I worked directly with stakeholders to gather reporting needs, build wireframes, and run full-on UAT feedback loops in Agile sprints.
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My intro to the professional data world—where I learned dashboards can actually save people hours.
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I built Power BI dashboards that loaded 50% faster and reduced manual reporting by 40%.
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Enabled teams to dive into their own insights with self-serve tools, slashing ad-hoc SQL requests by 60%.
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Also handled monthly churn reports, helping reduce customer drop-off by 15% with clear, actionable insights.
INCOIS(Indian National Center for Ocean Information Services)
Internship
2019
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Cleaned and organized geospatial datasets using Python, SQL, and Excel, improving data accuracy by 30% for marine research and coastal mapping
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Conducted correlation analysis between coral reef abundance and fish populations in the area, leveraging geospatial modeling and predictive analytics to support marine ecosystem research
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Developed interactive geospatial visualizations using Python (Matplotlib, Seaborn) and GIS tools, enabling marine scientists to track ecosystem changes more efficiently, improving data accessibility and reporting by 25%
Certifications
