Data Analyst - SQL, Python & BI Expert

VINAY GOUD SIDDAGONI

DATA ANALYST

*************@*****.*** • 603-***-**** • LinkedIn • Open to Relocate PROFESSIONAL SUMMARY

Data Analyst with around 5 years of experience delivering actionable insights across healthcare, inventory, and business analytics domains. Strong in SQL, Python, Excel, and BI tools to analyze large datasets, build governed dashboards, and support data-driven decision-making. Proven ability to improve data quality, forecasting accuracy, and operational KPIs through statistical analysis, automation, and stakeholder collaboration.

SKILLS

Programming & IDEs: Python, R, SQL, NoSQL, Jupyter Notebook, Visual Studio Code Data Processing & Cloud Tools: Pandas, NumPy, SSIS, AWS (S3, EC2, RDS, Redshift, QuickSight), Snowflake, BigQuery Machine Learning & AI: Scikit-learn, TensorFlow, Keras, NLP (Text Classification, Sentiment Analysis), Generative AI (LLMs, Prompt Engineering), Predictive Modeling, Recommendation Systems Statistical & Analytical Methods: Regression Analysis, Decision Trees, Random Forests, Support Vector Machines, Statistical Modeling, Hypothesis Testing, A/B Testing, Cohort Analysis, Time-Series Forecasting, Trend Analysis Visualization & BI Tools: Tableau, Power BI, Looker, Advanced Excel (Pivot Tables, Power Query, VBA, Lookups) Databases & Data Management: MySQL, SQL Server, MongoDB, SQL Server Management Studio Version Control & Collaboration: Git, GitHub, JIRA, Confluence, Slack WORK EXPERIENCE

Cardinal Health, USA January 2024 – Present

Data Analyst

Analyzed large-scale pharmaceutical distribution and inventory datasets using SSMS, Snowflake, and Python, enabling demand visibility across 20K+ SKUs and reducing stock-out incidents by 18% across regional fulfillment centers.

Engineered automated ETL pipelines leveraging SSIS, AWS S3, and AWS RDS to consolidate ERP, logistics, and supplier data into an enterprise data warehouse, improving data refresh cycles from daily to near-real-time (60% latency reduction).

Developed governed Tableau dashboards aligned with healthcare data governance standards, tracking KPIs such as order fill rate, cold-chain compliance, and backorders, accelerating operational decision-making by 35% for supply chain leaders.

Implemented time-series forecasting and predictive models in Python to project drug demand and replenishment needs, improving forecast accuracy by 22% and supporting inventory planning during demand surges.

Applied NLP-based text classification models using TensorFlow to examine distributor feedback, shipment issues, and incident logs, reducing manual issue triage effort by 40% and improving root-cause identification accuracy.

Collaborated cross-functionally with product owners and compliance teams using JIRA, enforcing data quality, data wrangling, and governance controls that improved reporting accuracy by 30% and ensured HIPAA-aligned analytics delivery. Adons Softech, India January 2020 – July 2022

Data Analyst

Extracted and consolidated multi-source client data using SQL Server and AWS RDS, integrating CRM, sales, and operational datasets that supported analytics for 5+ enterprise clients and improved reporting completeness by 25%.

Performed extensive data cleaning, transformation, and integration using Python (Pandas, NumPy) and Advanced Excel, reducing data inconsistencies by 30% and improving downstream model reliability.

Conducted exploratory data analysis (EDA) to identify revenue drivers, customer behavior patterns, and operational bottlenecks, delivering insights that contributed to 12–18% performance improvements across client KPIs.

Built interactive Power BI dashboards with drill-downs, DAX measures, and automated refreshes, cutting manual reporting effort by 40% and enabling real-time stakeholder visibility.

Applied statistical modeling, hypothesis testing, and A/B testing to evaluate feature changes and process optimizations, supporting data-backed decisions with 95% confidence-level validation.

Developed machine learning and NLP-based sentiment analysis models using Scikit-learn and Python, evaluating customer feedback and support tickets to improve satisfaction scoring accuracy by 20%.

Documented analytics workflows, data definitions, and business logic in Confluence, strengthening knowledge transfer, auditability, and reducing onboarding time for new analysts by 35%. EDUCATION

Masters of Science in Information Technology May 2024 Concordia University St. Paul, Minnesota, USA