Data Science Consultant Resume Atlanta, GA - Hire IT People

PROFESSIONAL SUMMARY:

Data enthusiast with knowledge in Data Science domain and ability to provide analytics and custom development for specific business use cases.
Combines knowledge of business, mathematics and analytics with hands - on experience of development of machine learning algorithms and data modeling to derive innovative solutions to enhance performance, productivity and quality of deliverables in any industry.
Skills include Machine Learning, Data Modeling, Business Analytics, Data Visualization.
Programming in R, Python, SQL, pySpark .
Visualizations using python, R-Programming, ggplot2, plotly, matplotlib, and Tableau for end-user ad-hoc reporting.
Able to create analytical models, algorithms, and custom software solutions based on accurate understanding of business requirements.
Works well with stakeholders, end-clients and customers, utilizing various methods to gather requirements such as interviews, workshops, and documentation review.
Build statistical models and BI reporting solutions which pull from a variety of data sources including structured and unstructured data.
Use of classification techniques and/or econometric forecasting techniques.
Application of machine learning, Naïve Bayes, Regression Analysis, market basket analysis and Random Forest machine learning techniques.
Experience in handling, and implementing statistical models on big data sets using cloud/cluster computing assets with AWS.
Creative thinking/strong ability to devise and propose innovative ways to look at problems by using business acumen, mathematical theories, data models, and statistical analysis.
Developed predictive models using Decision Tree, Random Forest and Naïve Bayes.
Development of regression, classification, and recommender systems with large datasets in distributed systems and constrained environments.
Experienced in Python to manipulate data for data loading and extraction and worked with python libraries like MatPlotLib, NumPy, SciPy, and Pandas for data analysis.
Proficient in using Python, R, SQL, Hive for extracting data for analysis purpose.
Excellent understanding Agile and Scrum development methodology.

SKILL:

Analytical Tools: SAS, STATA, Microsoft Excel Data AnalysisAdvanced Excel (VBA, Pivot table, Vlookup), Time Series Analysis, Facebook analytics tool, Google analytics

Programming Language: R, Python

Database: MySQL, Microsoft SQL Server, SSIS, SSRS, Hive query language

Analytical Methodologies: Linear Regression, MultiLinear Regression, Logistic Regression, Na ve Bayes Classifier, KNN classifier, K-MEANS clustering, PCA

AWS: EMR, Hadoop, HDFS, Spark (PySpark), Hive

Machine Learning Framework: TensorFlow 2.0, Torch

Microsoft Excel, Tableau, R: programming ggplot2, python Matplotlib, Seaborn

Command Language: Bash, Command line, Linux

MS Project, Agile methodologies: Scrum, SDLCs

EXPERIENCE:

Data Science Consultant

Confidential, Atlanta, GA

Responsibilities:

Used various web scraping techniques such as Beautiful-Soup, Selenium, cURL web scraping to collect more than 100k house data for a county of interest
Used data visualization tools to create meaningful graphs for client team
Cleaned the data and engineered features for house price prediction model
Used Machine Learning to create a house price prediction model with error rate of 6.8%

Tools: & Tech used: Jupyter Notebook (python), Agile Project Management, SDLCs,matplotlib, scikit-learn

Data Science Intern

Confidential, Dallas, TX

Responsibilities:

Conducted exploratory analysis to interpret trends or patterns in complex data sets
Used data visualization tools to create meaningful graphs for management team
Worked with deep learning engineers to design and create deep learning technologies
Assisted in developing code for deep learning for image analysis purpose
Worked within a scaled agile framework setting as a Scrum Master in-training

Tools & Tech used: Jupyter Notebook (python), Tableau, Agile Project Management, SDLCs, TensorFlow, Keras

Business Analyst

Confidential

Responsibilities:

Identified product seasonality trend and forecasted demand based on historical sales data
Performed market and competitor price analysis
Calculated financial performance indicators to support decision making
Negotiated with concerned stakeholders on behalf of the management
Studied target market segmentation and devised promotions accordingly

Tools & Tech used: Advanced Excel (VBA, Pivot table, Vlookup), Time Series Analysis

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