Data Analyst Machine Learning

Sandeep Vadlamudi

DATA ANALYST

+1-551-***-**** ******************@*****.*** Oklahoma, USA

https://www.linkedin.com/in/sandeep-vadlamudi-34383a2b3/

SUMMARY

Data Analyst with 3 years of experience in data-driven decision-making through advanced analytics, machine learning, and data visualization across business and healthcare domains.

Proficient in Python and SQL for comprehensive data analysis, data mining, and predictive modeling; skilled in statistical methods like ANOVA and hypothesis testing.

Experienced with Power BI, Tableau, and advanced Excel to design impactful dashboards and reports that support actionable insights and data storytelling.

Hands-on expertise in working with large datasets using MySQL, PostgreSQL, MongoDB, and Oracle; adept in ETL processes, data cleaning, transformation, and scalable cloud solutions on AWS, GCP and Azure.

Solid understanding of EHR systems (Epic, Cerner), health data standards (ICD-10, CPT, HL7), and compliance with HIPAA regulations, ensuring data security and integrity in healthcare analytics.

Collaborated with cross-functional teams including product, engineering, and operations to align analytics with organizational goals and deliver end-to-end data solutions.

Influenced executive decision-making by delivering actionable insights that increased operational efficiency, reduced customer churn, and drove strategic business initiatives.

SKILLS

Methodologies:

SDLC, Agile, Waterfall, SCRUM

Programming Language:

Python, SQL, R

Packages:

NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, TensorFlow, Seaborn, dplyr, ggplot2

Visualization Tools:

Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP), Looker, Qlik Sense

IDEs:

Visual Studio Code, PyCharm, Jupyter Notebook, IntelliJ

Database:

MySQL, PostgreSQL, MongoDB, SQL Server

Cloud Platform:

Amazon Web Services (AWS), Snowflake, MS Azure, GCP

Other Technical Skills:

SSIS, SSRS, SAS, SPSS, Machine Learning Algorithms, ETL\ELT Tools, Statistics, ServiceNow, Hadoop, Spark, Databricks, air flow MapReduce, Alteryx, Google Big Query, Power Query, dbt, Kafka, Zeppelin, Google Colab, ANOVA, Advance Analytics, OLAP & OLTP, SAS, Data Dog, Splunk, and Prometheus, MS Visio, Hypothesis Testing, Regression Analysis, Linear Algebra, Advance Analytics, Data Mining, Big Data, Data Integration, Data Interpretation, Data Pipeline, Data Visualization, Data warehousing, Data transformation, Data Governance, Clustering, Regression, A/B Testing, Star Schema, Data Cleaning, Data Wrangling, Git, GitHub, JIRA

Operating System

Windows, Linux, iOS

EXPERIENCE

Role : Data Analyst (Aug 24 - Present) OU Health, USA

Developed patient segmentation models using Python (Pandas, NumPy, Scikit-learn) and SQL to identify high-risk groups, resulting in a 40% reduction in hospital readmission rates.

Created interactive Power BI dashboards using DAX to visualize admission trends, readmission rates, and clinical outcomes, reducing report generation time by 50% and improving executive-level decision-making.

Optimized ETL pipelines with Apache Spark and Talend, improving data integration processes and reducing data processing latency by 35%.

Performed statistical analysis and A/B testing using SQL and Python (Statsmodels, SciPy) to evaluate patient intervention programs, increasing follow-up compliance rates by 20%.

Built predictive models using Scikit-learn and Python, applying Random Forest and Logistic Regression algorithms to forecast readmission risk and improve early intervention accuracy by 25%.

Designed anomaly detection solutions with Google BigQuery and SQL, achieving 58% precision in identifying unusual readmission cases and supporting proactive care management.

Implemented data governance using Informatica and Azure Purview, ensuring HIPAA compliance and reducing data access risks by 30%.

Collaborated with clinical teams and data scientists using Azure SQL Database, Azure Data Lake Storage, and Azure Data Factory to deliver actionable insights that improved patient retention by 15%.

Role : Data Analyst (Apr 21- Jun 23) Wipro, India

Developed a sales analytics system using Python and SQL Server to detect anomalies and improve forecast accuracy, resulting in a 15% reduction in forecast variance across regional sales data.

Created optimized SQL queries across SQL Server to aggregate and filter multi-source sales data, enabling more accurate and timely performance reporting.

Built automated ETL pipelines using Talend and Informatica for daily and monthly sales ingestion, reducing manual data processing time by over 50% and improving data consistency.

Designed interactive dashboards in Tableau to track revenue, product performance, and region-wise KPIs, increasing stakeholder engagement and reporting clarity.

Implemented demand forecasting models in Python using Pandas, NumPy, and Scikit-learn, which supported inventory optimization and reduced product stockouts in key regions.

Migrated high-volume sales data to Amazon Redshift and tuned SQL queries for performance, cutting dashboard load times by 60% and supporting faster data retrieval.

Enforced data quality standards using Alation for data cataloging and documentation, enhancing data transparency and trust across business users and analysts.

Automated recurring sales reports in Excel using VBA, Power Query, and Pivot Tables, improving reporting efficiency and eliminating repetitive manual tasks.

Conducted statistical analysis with SAS and SPSS to evaluate regional campaign performance using regression, ANOVA, and hypothesis testing, supporting data-driven marketing decisions.

Applied secure data handling practices aligned with internal governance standards, improving compliance and reducing risks associated with handling sensitive financial data.

Delivered training and created documentation to support business teams in using dashboards and interpreting KPIs, leading to improved adoption and decision-making across departments.

EDUCATION

Master of Science in Web Development ( computer science )

Oklahoma City University

Bachelor of Technology in Computer Science and Engineering

Rama Chandra College of Engineering India

PROJECT

DIAGNOSING CHRONIC KIDNEY DISEASE USING HYBRID MACHINE LEARNING TECHNIQUE

Developed an AI-driven diagnostic system using Python to detect chronic kidney disease early, applying hybrid machine learning techniques for enhanced prediction accuracy.

Built and trained predictive models on real-time health datasets, improving diagnostic precision and supporting clinical decision-making.

Implemented adaptive algorithms and automated data pipelines for risk assessment and efficient data classification.

Utilized SQL for data preprocessing and model input optimization, improving pipeline performance and enabling real-time analytics.

Collaborated with cross-functional teams to align model development with business strategy, influencing executive decision-making, increasing operational efficiency, and reducing patient churn.

ADVANCE TRAFFIC FLOW CONTROL SYSTEM

Designed and deployed a machine learning-based intelligent traffic control system leveraging real-time IoT sensor data, optimizing signal timings dynamically to reduce urban congestion and improve commuter experience.

Developed congestion prediction models and adaptive signal adjustment algorithms using Python (scikit-learn, NumPy, Pandas), enabling proactive traffic flow management with measurable reductions in average wait times.

Engineered an emergency vehicle priority lane system with rule-based logic to detect and prioritize emergency vehicles in real-time, significantly enhancing response times and public safety.

Conducted comprehensive traffic pattern analysis and built interactive dashboards using Power BI and Plotly to visualize traffic flow metrics, supporting data-driven decisions for smart city initiatives.

Deployed the entire solution on Microsoft Azure, utilizing Azure IoT Hub, Azure Functions, and Azure Stream Analytics for scalable, cloud-native processing—delivering real-time insights and operational efficiency for municipal stakeholders.

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