Business Solution Analyst Resume

TECHNICAL SKILLS

Programming Languages: SQL, Python (NumPy, Pandas, scikit - learn, seaborn, matplotlib PySpark), R (ggplot2, dplyr), Unix, Java

Database: Oracle, MYSQL, Teradata, SQL Server, Redshift, Snowflake, Hadoop, Hive, Pig, Sqoop

Tools: Tableau, SAS JMP, Visio, MS Office, SAS Enterprise Miner, Informatica Power Center, Business Objects, Eclipse, Power BI

Analytical Skills: Data cleaning, Data Analysis, Data Mining, Hypothesis Testing, Statistical Analysis and Modeling, Machine Learning, Clustering, Logistic and Linear Regression, Time Series Forecasting, NLP, CNN, XGBoost, Text Analytics, Data Visualization

Cloud Environment: AWS, Azure, EC2, CC2, S3, Athena, Glue, Google Cloud Platform, Big Query

PROFESSIONAL EXPERIENCE

Confidential

Business Solution Analyst

Responsibilities:

Designed the data dictionary, schema & data models for pharmacy and claims data. Based on the analyses, transformed & loaded the data into the data warehouse. Created analytical reports to visualize business insights
Segmented members using K-means clustering to develop platform for Diagnosis Recommendation system

Confidential

Data Science Intern| Python

Responsibilities:

Extracted unlabeled biomechanical data from sensors (fitted to garments) and then extracted features of athlete’s fitness. Performed exploratory data analysis to find patterns and correlations between different knee motions. Implemented Naïve Bayes model with an accuracy of 88.4% to classify athletes as injured/non-injured

Confidential

Analytics Consultant

Responsibilities:

Tracked and reported website behaviors and campaign performance for client leveraging Google Analytics.
Used A/B Testing and unsupervised machine learning to streamline the marketing process by navigating traffic data for over 100,000 potential customers and identifying key segments to target

Confidential

IT Data Analyst

Responsibilities:

Developed a Python script and data pipeline to migrate terabytes volumes of PeopleSoft data from Oracle to Azure. Reduced manual labor work by 148 hours in processing business of a single client
Integrated the customer banking data, created a Random Forest model to target customers with high probability to purchase loan. Visualized insights & recommendations to various stakeholders to aid strategic decisions for improvements in customer selection. This helped the bank increase their revenue by $0.85B

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