Rami Shehadah

**** ***** ***, *** ****, CA *****

408-***-**** / ac4aiu@r.postjobfree.com

Linkedin URL: https://www.linkedin.com/in/rami-shehadah-28a97866/

EDUCATION

M.S. Biomedical Engineer Focus in Data Science / Machine Learning, San Jose State University Dec, 2017

B.S. Biomedical Engineer, San Jose State University, San Jose CA May, 2014

RELATED COURSEWORK: Python Programing, Machine Learning, DataCamp for Data Science, Statistics for Data Science, Coursera Andrew Ng Machine learning

SKILLS:

PROGRAMING LANGUAGE: Python, MATLAB, SQL, SQLALCHEMY, JMP (SAS),

STATISTICAL ANALYSIS METHODS: Probability, Permutation Testing, Hypothesis Testing, A/B testing, t Test, Statistical Distribution, ANOVA, chi-square, Bayes Theorem, Bootstrap, P-value, PCA, PLS

DATA SCIENCE/MACHINE LEARNING: Pandas, Numpy, Matplotlib, ggplot, Seaborn, Logistic Regression, Decision Tree, Linear Regression, Decision Tree, Data Mining, Data Visualization, K-nearest (KNN), Cross Validation, Scipy

DATA SCIENCE / MACHINE LEARNING EXPERIENCE

Work with different machine learning algorithms to analyze and build predictive models on a wide range of unsupervised/supervised data sets using python’s machine learning libraries

Perform data preprocessing by using sklearn.preprocessing methods on data sets to reducing model error by 5-10%

Understand significant features of a data set by running sklearn.feature_selection, filter methods (ANOVA, and chi-square) and embedded methods to help further reduce model error by 15-30%

Run cross validation on data sets to truly understand predictive model performance

Run principal component analysis (PCA), partial least square (PLS) and other methods to understand correlation between features within dataset

WORK EXPERIENCE

Pacific BioSciences, Menlo Park CA September 2015– Present

R&D Data Analyst, Device Development Engineer

Perform quantitative analysis and process 1-10GB DNA sequencing data using python/JMP to identify trends that help drive decision making by the team

Develop predictive models from supervised/unsupervised data by using data science machine learning techniques

Develop scripts using python to automate data collection from different data pipelines to optimize data analysis

Develop, execute and analyze Design of Experiments (DOE) data to help understand chip physical parameters and sequencing system performance with test planning, data mining and troubleshooting

Present PowerPoints with great data visualization skills on sequence performance findings to different teams, directors and executives to influence decision making by the company

Intuitive Surgical April 2014- September 2015

R&D Test Engineer

Responsible for running and documenting engineering, beta and life testing on Intuitive surgical instruments

Used MATLAB to code and run scripts to communicate with the da Vinci system

Analyze data to determine beta values using Weibull distribution and wrote reports that include statistical data

Collect and analyzed data for friction, torque and range of motion for the instruments

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