Data Microsoft Office

Professional Summary:

Microsoft Certified Data Science professional with great passion in Data Analytics specializing in Data Science, Azure Machine Learning and Tableau.

Worked on analyzing large datasets on distributed databases and developing Machine Learning algorithms to gain operational insights and present them to the leadership.

Extensively involved in Data preparation, Exploratory analysis, Feature engineering using Supervised and unsupervised modeling.

Well versed with Linear/non-linear, regression and classification modeling predictive algorithms

Actively involved in Model selection, Statistical analysis using Gretl Statistical Tool

Created dashboards as part of Data Visualization using Tableau

Performed preliminary data analysis using descriptive statistics and handled anomalies such as removing duplicates and imputing missing values using Talend tool

Performed Dimensionality reduction using near zero variance and correlation techniques.

Validate the consolidated data and develop the model that best fits the data. Interpret data from multiple sources, consolidate it, and perform data cleansing using R Studio

Performed multiple Data Mining techniques and derive new insights from the data

Team player with good logical reasoning ability, coordination and interpersonal skills

Able to complete projects independently as well as within a team environment

Team builder with excellent communications, time & resource management & continuous client relationship development skills.

Technical Expertise:

R Programming * R Studio * Azure Machine Learning * Tableau * Talend Data Preparation Tool * Github

PL/SQL * Microsoft Office * MS-Access *

Nestle Waters Dec’ 10 – Current

Associate Data Analyst

Responsibilities:

Apply computational methods to mine and model data generated from daily and hourly QC tests

Update databases, correcting database errors to find corrective actions

Create statistical models on the factory generated data to improve production efficiency.

Trained new hires on GLP, GMP, Factory Hygiene, data entry at New Hire Orientation

Designed applications of Machine learning, Statistical Analysis and Data visualizations with challenging large data processing problems.

Performed the computations, log transformations, feature engineering, and Data exploration to identify the insights and conclusions from complex data using R- programming in R-studio

Implemented predictive models using machine learning algorithms linear regression and linear boosting algorithms and performed in- depth analysis on the structure of models, compared the performance of all the models and found tree boosting is the best for the prediction.

As a Subject Matter Expert, Analyzed chemicals and packaging components per SOP and FDA regulations using Laboratory Information Management System (LIMS) for data analysis

Applied concepts of R-squared, R.M.S.E, P-value, in the evaluation stage to extract interesting findings through comparisons.

Performed in-depth statistical analysis and data mining methods using R, including Cluster analysis, Logistic Regression, and boosting models

Proficient in the entire CRISP-DM life cycle and actively involved in all the phases of project life cycle including data acquisition, data cleaning, data engineering,

Extensively used Azure Machine Learning to set up the experiments and creating Web services for the predictive analytics

Performed feature scaling, feature engineering and statistical modeling.

Worked on writing complex SQL queries in performing Data analysis using window functions, joins, improving performance by creating partitioned tables,

Prepared multiple dashboards using Tableau to reflect the data behavior over period of time Analyzed and worked with all aspects of regression models (OLS etc.)

Responsible for working with stakeholders to troubleshoot issues, communicate to team members, leadership and stakeholders on findings to ensure models are well understood and optimized.

Marshfield Food Safety LLC Jun ‘08 to Dec '10

Data Quality Analyst

Responsibilities:

Designed, modeled, validated and tested statistical algorithms against various data sets including behavioral data and deployed predictive models using R-studio

Performed Data Transformation method for Rescaling and Normalizing variables.

Applied different Machine Learning algorithms/methods on data sets to predict credit risk, fraud detection, customer churn, and target marketing.

Worked on data to increase cross-& up-sell revenues, enhance customer value or reduce non-credit losses.

Contributed implementing models to identify, extract, summarize, and reduce or categorize the relevant qualitative financial input information like sentiment/feedback/news according to specific structures (templates) from a source text (digital news) to support decision making.

Analyzed, transformed, and contextualized a variety of ingested data - social data, GIS data, POI& AOI data, and some consumer behavior data for building direct marketing predictive models.

Analyze customer consuming behavior and discover value of customers.

Applied customer segmentation with Clustering algorithms and develop geo-demographic customer segmentation models.

Developed personalized products recommendation with Machine Learning algorithms including Collaborative filtering and Boosting Tree, to better meet the needs of existing customers and acquire new customers.

Perform Boosting method on predictive models to improve/optimize model performance.

Building, publishing& scheduling customized interactive reports& dashboards using Tableau Server.

Deliver Interactive visualizations/dashboards using ggplot and Tableau to present analysis outcomes in terms of patterns, anomalies and predictions.

Created multiple workbooks, dashboards, and charts using calculated fields in Tableau to meet business needs.

Prepare comprehensive documented observations, analyses and interpretations of results including technical reports, summaries, protocols and quantitative analyses. Working closely with marketing team to deliver actionable insights from huge volume of data, coming from different marketing campaigns and customer interaction matrices such as web portal usage, email campaign responses, public site interaction, and other customer specific parameters.

Characterizing false positives and false negatives to improve a model for predicting customer churn rate.

Education

(MBA), Amberton University, Expected Graduation Feb 2018

B.S. Chemistry, Concentration: Biotechnology, West Texas A&M University

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