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Data Scientist Resume

Princeton New, JerseY


  • Process - oriented Data Analyst with over 6 years of experience in IT and comprehensive industry knowledge on Data Analysis, Data Manipulation, Machine Learning, Statistical Modeling, Predictive Analysis, Big Data Analytics, Market Analysis, Data Visualization and Business Intelligence.
  • Experience in transforming business requirements into data models and further providing actionable insights to drive business growth
  • Advanced proficiency in SAS, R, Python, SQL, Excel, Tableau, Google Analytics along with Big Data technologies like Hadoop, Hive, Pig, Sqoop, Spark (Scala) etc.
  • Expertise in performing Feature Selection, Linear Regression, Logistic Regression, k-Means Clustering, Classification, Decision Tree, Supporting Vector Machines (SVM), Random Forest, and Neural Network algorithms to train and test the huge data sets.
  • Proficient in designing and creating various Data Visualization Dashboards, worksheets and analytical reports to help users to identify critical KPIs and facilitate strategic planning in the organization utilizing Tableau Visualizations according to the end user requirements.
  • Strong knowledge in Statistical methodologies such as Hypothesis Testing, Principal Component Analysis (PCA), Sampling Distributions, ANOVA, Chi-Square tests, Time Series, Factor Analysis, Discriminant Analysis.
  • Demonstrated ability to identify root causes of problems, consider both the long and short-term impact of proposed solutions and develop workable solutions. Ability to manage (Multiple) project tasks with changing priorities and tight deadlines. Ability to work well in a team. Open to Team related tasks, team meetings, team interaction and conference calls.
  • Work well with a wide variety of people at various levels, foster cooperation and collaboration among individuals in the work unit, help team resolve conflicts constructively and ability and willingness to communicate when help is needed. A Self-starter with a positive attitude, willingness to learn new concepts and acceptance of challenges.
  • Direct Interaction with customers. Clear written and oral presentation of ideas, proposals and concepts. Ability to articulate alternative solutions. Strong listening, interpersonal and analytical skills.


Software and Programming Languages: Advanced MS Excel, SAS, R, Python (NumPy, Pandas, Scikit etc.), MySQL, Oracle, AWS, Linux, Tableau, QlikView, Google Analytics, Adobe Analytics, Big Data Technologies (Hadoop, Sqoop, Hive, Pig, Spark, Scala), etc.

Machine Learning Techniques: A/B Testing, Linear and Logistic Regression, Decision Trees, Support Vector Machines, Artificial Neural Networks, Unsupervised Learning, Time Series, Feature Selection, and Transformation, etc.


Confidential, Princeton, New Jersey

Data Scientist


  • Provided actionable insights for Fitness Center Management to define new business strategies geared towards improving their programs, creating effective marketing campaigns and offering personalized rewards to members
  • Createddashboard using Tableau, in order to quickly spot customer behavioral aspects and preferences
  • Executed predictive analysis using Python on 100,000 data points to identify top customers more likely to churn next month
  • Utilized data cleansing methods such as imputation, transformation and reducing skewness. Pinpointed key influencing variables using various machine learning models such as logistic regression, gradient boosting algorithm etc.
  • Advised on a Gradient Boosting Algorithm model with 86% accuracy for predicting customer churn
  • Improved visitor experience toultimately drive higher volumes of customer leads using Google Analytics

Environment: MS Excel, Python, Tableau, Google Analytics

Confidential, Frisco, Texas

Data Analyst


  • Developed financial data analysis leveraging MS Excel and Apptio TBM software to demonstrate the financial impacts of business decisions to over 500 internal clients
  • Optimized IT service costing platform by introducing new services to be billed, improving transparency to stakeholders
  • Collaborated with IT Executives, leadership and other stakeholders on IT Total Cost of Ownership, financial metrics, and cost transparency initiatives
  • Provided support to the stakeholders through technical expertise on data analytics, driving process and data improvement
  • Enhanced organization’s expenditure forecasting ability including assessment of trends, identification of variance drivers and feasible action plans

Environment: MS Excel, Apptio


Business Data Analyst, Operations and Strategy


  • Produced digestible business intelligence and actionable information leading to revenue acceleration
  • Captured and surfaced the best data and information to make optimal decisions, driving a rapid expansion of its world-wide sales team
  • Spearheaded resource planning and analytical support to the sales team leading to better customer service and cloud product sales
  • Defined, built, and scaled metrics and analytical insights to measure the success and drive the day-to-day behavior for the AWS Business Development Team

Environment: MS Excel, Salesforce


Data Analyst, SAS


  • Architected strategy for expansion of an ice-cream company by tapping into the customer behavior and characteristics
  • Performed K-means clustering on behavioral data to segment and identify the most profitable customers
  • Built logistic regression model with 89% accuracy to detect key demographic variables that help discriminate between more profitable and less profitable customers

Environment: SAS, Tableau


Programmer Analyst


  • Led team of 5 to strategize client’s, LexisNexis, transition from RDBMS to Hadoop platform
  • Implemented data importing using SQOOP from MySQL to HDFS
  • Orchestrated pipeline to structure, transform and cleanse data using SparkSQL
  • Performed text analytics on customer data using Hive and provided recommendations to improve customer satisfaction index by 7%

Environment: SQL, Hadoop, Sqoop, Hive


Data Analyst, R programming


  • Upgraded analysis metrics on oil production data, increasing oil production efficiency by 9%
  • Implemented Machine Learning using R to forecast production for existing wells based on production history, pressure data, geological maps etc.
  • Predict well production performance using nearby wells which were drilled in analogous geological conditions
  • Recognized the most significant parameters impacting the oil production efficiency

Environment: MS Excel, R, Tableau


Data Analytics Intern


  • Analyzed data to throw insights on effectiveness of campaigns running onmarketplace
  • Extracted relevant information from large databases using SQL queries to detect fraud patterns

Environment: SQL, HTML, PHP, Google Analytics

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