Data Analyst

P a g e * *

Pari Manouchehri Data Scientist and Machine learning Engineer

Visa Status: US Citizen Email: *********.**@*****.*** Portfolio: https://parichehrma.github.io/

Bellevue, WA GitHub: https://github.com/parichehrma/parichehrma.github.io

(631) 704 - 8299 LinkedIn: https://www.linkedin.com/in/pari-manouchehri/ I am a Data Scientist proficient in supervised and unsupervised learning. I have sound experience in Python, deep learning

(TensorFlow & PyTorch), NLP and PySpark which have allowed me to develop big dataset analysis skills. Also possess strong background in Excel and Power BI Data Visualization. Technical Skills

Statistics & Experimental Design: linear/ logistic regression, A/B testing, predictive analytics, Bayesian statistics, hierarchical models, categorical inference

Data Cleaning/Processing/Visualization: Missing data imputation, Class-Imbalance, Transformation, Power BI, Excel. Machine Learning: Feature engineering/selection, Dimensionality reduction, Supervised & Unsupervised learning, Classification, Hyper-parameter tuning, Neural networks, Deep learning(TensorFlow & PyTorch), NLP Tools: Python (scikit-learn, Pandas, NumPy, Seaborn, Matplotlib), PySpark Database & Development Tools: Mining and Warehousing (PostgreSQL), Git/GitHub Projects

Corona Virus : Analyzed COVID-19 world data and significant risk factors including confirmed cases/deaths rate forecast and make predictions by different machine learning methods with Python. Utilized EDA, A/B testing, dimensional reduction, supervised models to solve classification problems (Logistic Regression, Random Forest, Support Vector) & Unsupervised learning clustering (Kmeans, Hierarchical Clustering, DBSCAN and GMM). Also applied some of these models with Pyspark.

Predicting Attack for Network Intrusion Detection : Purpose of the project was to analyze Network Intrusion Detection Attack and perform anomaly detection. Utilized EDA, Feature Engineering likes one-hot, Applying PCA, A/B testing, various supervised models to solve classification problems with Python (Logistic Regression, KNN, Random Forest, Gradient Boosting models) and optimize models by GridSearch.

Customer Segmentation Based on Their Credit Card Usage Behavior : Customer segmentation using behaviors to define marketing strategy with unsupervised learning by Python. Also applied dimensional reduction (PCA, TSNE, UMAP) and various unsupervised clustering.

Experience

Petroleum Industry Health Organization (PIHO) Iran Data Analyst Sep 2007- Aug 2011 & Jul 2013– Feb 2014 Identified valuable data sources and design collection processes systems for gathering data and analyzed raw data to identify trends. Also presented outcomes by data visualization techniques & statistical analysis of data. Education

Thinkful Remote

Data Science Sep 2019-May 2020

Learned industry best practices and modern data science standards alongside a senior data scientist mentor. Developed and presented projects involving exploratory data analysis, data cleaning, machine learning, statistical modeling, study design, statistics and probability. Universiti Teknologi Malaysia (GPA: 3.7) KL, Malaysia Master’s Degree - Computer Science (Information Security) Jun 2013 Azad University Iran

Bachelor’s Degree - Software Engineering Sep 2006

Certificates

Microsoft Professional Program in Data Science including:

Microsoft : Analyzing and Visualizing Data with Power BI

Microsoft : Analyzing and Visualizing Data with Excel

Microsoft : Essential Statistics for Data Analysis using Excel

Coursera: Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Udemy: Amazon Web Services Certified Cloud Practitioner (AWS)

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