Data Engineer

Fanus Arefaine

San Jose, CA ***** *****.**********@*****.***

702-***-**** linkedin.com/in/fanus-arefaine/

EDUCATION

Masters in Software Engineering (specializing in Data Science), San Jose State University, San Jose, CA May 2021

Bachelors in Electrical and Electronics Engineering, Eritrea Institute of Technology, Asmara, Eritrea June 2015

TECHNICAL SKILLS

Programming Languages: Python, R, C++, MATLAB, SQL

Machine Learning/Data Science tools: TensorFlow, Keras, Numpy, Pandas, Matplotlib, Pyplot, Seaborn, NLTK,

Scikit-learn, Scipy, Spark, Time-Series Analysis and Forecasting, Big Data,

Data Visualization, Tableau/PowerPoint/Excel, Quantitative analysis

Platforms: Git, Docker, AWS

Databases: Relational Databases, MySQL

PROFESSIONAL EXPERIENCE

Red Sea General Construction and Development Share Company, Massawa, Eritrea Oct 2015 – Jul 2019

Machine Learning Engineer

Augmented traditional construction cost estimation by 20% using Random Forest predictive model

Catalyzed production rates and increased profit by 22.5% using hierarchical clustering for customer segmentation

Directed and mentored new hires in developing and advancing theoretical and professional expertise

Eritrea Electric Corporation, Asmara, Eritrea June 2014 – Sep 2014

Data Science Intern

Maximized safety by integrating an association rule-based solution to detect and eliminate electric surges and transients

Enhanced customer experience after delivering insights about customer satisfaction and related features

Polytech Vocational Training Center, Asmara, Eritrea June 2013 – Sep 2013

Electronics/ Data Analytics Intern

Accelerated workflow by optimizing circuit tracing, detecting and servicing faulty electronic components

Alleviated business value by generating analytic insights for Photovoltaic system distribution

PROJECTS

Natural Language Processing with Disaster Tweets (Numpy, Pandas, Sklearn, NLTK, Keras) June 2020

Built a deep learning classifier model, whether each tweet represents a disaster or not, for an ongoing Kaggle challenge

Achieved 81.62% accuracy on more than 10k tweets using Recurrent Neural Network implemented with Keras

Grape Disease and Pest Detection and Classification (TensorFlow, Keras, Numpy, Sklearn) Feb 2020 – May 2020

Engineered a deep learning model to reduce monetary and resource losses due to disease and pests in grape farms

Achieved 99.93% accuracy on unseen ~3K images using transfer learning with ResNet50 model

DeepSolar California (Numpy, Pandas, Sklearn, Seaborn, Matplotlib, Seaborn)

Feb 2020 – May 2020

Created classification and predictive models for solar system distribution in the State of California

Achieved classification accuracy of 97% on unseen data and regression prediction of R2 value of 0.7

CERTIFICATIONS AND ACHIEVEMENTS

Natural Language Processing with Python for Machine Learning

Amazon Web Services for Data Science and Machine Learning

Deep Learning Specialization: CNN, Improving Deep Networks, Hyperparameter tuning, Regularization and Optimization, Structuring Machine Learning Project, Sequence Models, LSTM, Computer Vision

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