PROFESSIONAL SUMMARY

• Data Scientist with experience in Artificial Intelligence, Machine Learning, Deep Learning, Data Mining and Predictive Analytics and Decision Science. Businesses require professionals with specialized knowledge to deal with this exponentially growing data. Understanding the underlying science of data equips one to apply the same in a diverse set of problem statements in a variety of fields
• Experience as Data Scientist specialized in analytics with data science, Azure Machine Learning, Python, R skill and SDLC, AGILE Methodologies, AGILE Manifesto and Scrum processes.
• Solid experience in applying and implementing machine learning algorithms and concepts such as: K - means Clustering (varieties), Gaussian distribution, decision tree etc.
• Experience in using Data Analytic tools like Alteryx, Micro Strategy and Knowledge on Adobe Analytics.
• Analyze Business problems from data and create predictive models Using Azure Machine learning
• Developed Tableau visualizations and dashboards using Tableau Desktop.
• Hands on experience developing Interfaces in Mule Any Point Platform Consuming RESTful services, RESTful with RAML and SOAP Web Services.
• Capable of setting and running environments on Azure and AWS cloud technology.
• Undertook HIPAA training for safeguarding US medical and healthcare information.
• Expertise in leveraging data mining and machine learning techniques to gather insights from internal and external data.
• Documented methodology, data reports and model results and communicated with the project team manager to share the knowledge.
• Expertise in developing innovative capabilities that result in data driven decision making for organizations in the new age of Data Science, Big Data and Predictive Analytics
• I wish to delve deeper, continue learning and expand my knowledge base thereby innovating methods to enhance data driven decision making.

TECHNICAL SKILLS:-

Expertise: Scikit-learn, NLTK, Numpy, SciPy, OpenCv, Deep learning, NLP, RNN, CNN, Tensor flow, Keras, matplotlib.

Machine Learning Algorithms: Linear Regression, Logistic Regression, Decision Trees, Random Forest, K Means Clustering, Support Vector Machines, Gradient Boost Machines &XGBoost

Cloud Technology: Azure, AWS (Amazon Web Services)

Data Analysis Skills: Data Cleaning, Data Visualization, Feature Selection, Pandas

Programming Languages: Python, SQL, R, Matlab, C++, Java

Other Programming Knowledge and Skills: Elastic Search, Data Scraping, RESTful-Api

Tools: Toad, Erwin, AWS, Azure, Mule Soft, Alteryx, Tableau, Adobe Analytics, Anaconda

PROFESSIONAL EXPERIENCE

Confidential, Kansas City, MO

Data Scientist

Responsibilities:

• Design, develop and produce reports that connect quantitative data to insights that drive and change business
• Analyzed data using data visualization tools and reported key features using statistic tools and supervised machine learning techniques to achieve project objectives.
• Designed dashboards with Tableau and provided complex reports, including summaries, charts, and graphs to interpret findings to team and stakeholders
• Progressive and experienced background in analytics and root cause analysis.
• Work independently or collaboratively throughout the complete analytics project lifecycle including data extraction/preparation, design and implementation of scalable machine learning analysis and solutions, and documentation of results.
• Developed and deployed Machine learning as a service on Microsoft Azure cloud service.
• Performing statistical analysis and building statistical models in R and Python using various Supervised and Unsupervised Machine learning algorithms like Regression, Decision Trees, Random Forests, Support Vector Machines, K- Means Clustering and dimensionality reduction.
• Evaluated and optimized performance of models, tuned parameters with K-Fold Cross Validation.
• Experience with AWS services, Azure, EC2, redshift and EMR.

Confidential, MO

Data Scientist

Responsibilities:

• Trained Data with Different Classification Models such as Decision Trees, SVM and Random forest
• Under supervision of Sr. Data Scientist performed Data Transformation method for Rescaling and Normalizing Variables
• Developed a predictive model and validate Neural Network Classification model for predict the feature label
• Conduct deep and continuous exploration of high-volume heterogeneous data.
• Work experience with large-scale data: many rows, many features and many categorical variables.
• Apply exploratory data analysis, engineering techniques, and machine learning to solve high-visibility problems.
• Analyzing transaction data to cluster users into segments and develop different marketing strategies for each cluster.

Confidential

Data Scientist

Responsibilities:

• Worked as Jr Data Scientist on projects relating to U.S. Healthcare Domain (Billing and Claims) data to enhance risk assessment in Medicare reimbursement as a key customer financial tool.
• Trained Data with Different Classification Models such as Decision Trees, SVM and Random forest
• Under supervision of Sr. Data Scientist performed Data Transformation method for Rescaling and Normalizing Variables
• Developed a predictive model and validate Neural Network Classification model for predict the feature label
• Performed Boosting method on predicted model for the improve efficiency of the model
• Conduct deep and continuous exploration of high-volume heterogeneous data.
• Work experience with large-scale data: many rows, many features and many categorical variables.

Confidential

Data Analyst

Responsibilities:

• Work with large amounts of data: facts, figures, and number crunching and translate the data into understandable format.
• Coordinated with testers and helped in the process of Integration testing.
• Apply exploratory data analysis, engineering techniques, and machine learning to solve high-visibility problems.
• Analyzing transaction data to cluster users into segments and develop different marketing strategies for each cluster.
• Worked with sales and Marketing team for Partner and collaborate with a cross-functional team to frame and answer important data questions