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Data Science & Analyst Resume

St, LouiS


  • 4+ years of academic and professional experience in Data Analytics,Predictive Analytics, Data Visualization and Python Developer.
  • Skillful in analyzing and interpreting large data sets to provide data directed approaches to accelerate business growth.
  • Proficient in using Predictive Analytics, Data Mining algorithms, and Cloud - Based Technologies.
  • Working Experience in Data Modeling, Database Design, ER Models, Relational database, Normalization & SQL.
  • Extensive experience working with relational databases like SQL, MySQL.
  • Skillful in using Python libraries Pandas, NumPy, MatPlotLib, Seaborn,Scikit Learn,NLTK,Keras,TensorFlow.
  • Experienced in using BI visualization tools such as Tableau, Power BI.
  • Expertise in using statistical models such as Hypothesis Testing, ANOVA, Regression, and A/B Testing.
  • Hands-on experience in using google analytics for web based or digital marketing analytics.
  • Experience in Data collection, Visualization, Analysis, Hypothesis Testing, Computational & Quantitative Methods, Linear Regression.
  • Expertise in using data mining models like Dimension Reduction, Association Rule Mining, Logistic Regression, KNN, Random Forest, SVM, Decision Trees, Clustering, K-Means Techniques.
  • Hands-on implementation experience in NLP, Document Representation, Text Categorization, Sentiment Analysis, Topic Modeling, Text Visualization.
  • Practice in Predictive analytics using Forecasting, Autocorrelation, Exponential Smoothing, Regression, ARIMA.
  • Good Knowledge of deep learning models like DNN, CNN, RNN, LSTM, GAN, Transfer Learning.
  • Strong skills in Data Analysis, Data Requirement Analysis, and Data Mapping for ETL process.
  • Strong documentation and analytical skills, strong problem-solving skills.


Data Analytics | Database Management | Data Mining | Predictive Analytics | Machine Learning | Data Visualization Business Intelligence | Data Warehousing | Prescriptive Analytics | Sentimental Analysis |Statistical Analysis | Natural Language Processing | Deep Learning | Big Data | Data Structures & Algorithms | CRM - Salesforce


Languages: Python, R, SQL, Spark,C

Tools: Salesforce, AWS, Git,Alteryx,Jira

Databases: Oracle, MySQL, Microsoft SQL Server, MS Access.

Visualization: Power BI, Tableau, Excel, Google Sheets, Salesforce Dashboards, Python, R

Machine Learning: Classification, Regression, A/B testing, NLP, Statistical Analysis, Hypothesis Testing, Clustering.

Package Libraries: Pandas, NumPy, Scikit, MatPlotLib, ggplot2, TensorFlow, Keras, Seaborn, NLTK.

Web Frameworks: Rshiny, Flask


Data Science & Analyst

Confidential, St. Louis


  • Worked on Developing various custom objects using Salesforce for Customer Success.
  • Used Data Loader to import and export data.
  • Created and maintained reports and dashboards across business needs and monitored data quality.
  • Identified & implemented Salesforce best practices to increase business performance relating to efficiency & improved service.
  • Used Python Libraries like Flask for web-based dashboard developments.
  • Developed REST API’s to create authentication for the web application.

Jr. Python Developer



  • Worked on development of customer support and complaint registration system. This is a Customer feedback and complaint management system.
  • Designed, developed, tested and maintained data management system website using Python and MySQL.
  • Validated scripts - sometimes challenging as it demanded many web-based logics rather than correlation and parameterization.
  • Used Python Database API's to access database objects.
  • Wrote python scripts to parse XML documents and load the data in database.
  • Reduced the amount of internet bandwidth used while loading images using Neural Network.
  • Used both lossy and lossless compression techniques to score the model
  • Assisted in architecture development, debugging, and testing. Also Validated data quality before presenting insights.
  • Involved in data gathering and interacted with users to understand the requirements, Analyzing needs for user community.
  • Developed Text Analytics on Women’s E-Clothing Review using Sentimental Analysis
  • Used various Machine learning concepts (SVM, Decision Tree, Random Forest, AdaBoost and Naïve Bayes) and statistical concepts to build the model.

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