Data Cleaning, Data Wrangling,Inferential Statistics, Descriptive Stat

Skills

Work History

A versatile and accomplished Machine Learning Engineer with 10 years of experience. Proficient in a wide array of machine learning techniques, including Supervised, Unsupervised, Reinforcement, and Deep Learning models, as well as specialized methods like K-Means, PCA, NLP, Anomaly Detection, Time Series Analysis, GANs, Transfer Learning, Bayesian Methods, and Reinforcement Learning Algorithms. Adept at using programming languages and tools such as Python, R, MATLAB, TensorFlow, Pandas, NumPy, SciPy, and various data visualization tools like Tableau, Power BI, and ggplot2. Proven track record of developing innovative solutions, optimizing processes, and driving business value through machine learning and data-driven insights. Learning models (Supervised, Unsupervised,

Reinforcement, Deep Learning), K-Means, Principal

Component Analysis (PCA), Natural Language

Processing (NLP), Computer Vision, Anomaly

Detection, Time Series Analysis, Generative Adversarial Networks (GANs), Transfer Learning, Bayesian

Methods, Reinforcement Learning Algorithms (Q-

Learning, Deep Q-Networks, Policy Gradient Methods) Data Cleaning, Data Wrangling,Inferential Statistics, Descriptive Statistics, Data Visualization, Probability Theory, Experimental Design, Regression Analysis,

Clustering, Text Mining, Sentiment Analysis, Network Analysis, Survival Analysis, Dimensionality Reduction, Big Data Technologies (Hadoop, Spark), Cloud

Computing (AWS, GCP, Azure)

Python, R, MATLAB, TensorFlow, Pandas, NumPy, SciPy, Matplotlib, Seaborn, ggplot2, Plotly, D3.js, Jupyter, Tableau, Power BI, Excel, SAS, RapidMiner, KNIME,

H2O, Apache MXNet, Caffe, Theano

2022-01 - Current Machine Learning Engineer

Mavericks United, Staffing Agency

Intezar Hussayne

Manager, Machine Learning Engineering

Address United States (US National)

Phone 973-***-****

E-mail *********@*****.***

Healthcare: Utilized supervised and unsupervised learning models to analyze and predict patient outcomes. I leveraged Natural Language Processing (NLP) to extract meaningful information from electronic health records and implemented Time Series Analysis to forecast patient admission rates. My technical expertise includes Python, TensorFlow, Pandas, and NumPy for data processing and modeling, as well as Tableau and Power BI for data visualization and reporting.

Education: Focused on developing adaptive learning systems using Reinforcement Learning algorithms such as Q-Learning and Deep Q-Networks. My work involved the use of NLP and Deep Learning to create intelligent tutoring systems that can adapt to individual student needs. I employed Python, R, TensorFlow, and Jupyter for data analysis and model building, while utilizing visualization tools such as ggplot2 and Plotly for data presentation. Payments: I utilized Anomaly Detection techniques and supervised learning models to detect and prevent fraudulent transactions. I employed Principal Component Analysis (PCA) for dimensionality reduction and K-Means for clustering analysis. My technical stack included Python, R, Pandas, SciPy, Matplotlib, Seaborn, and Excel for data processing and analysis. 2018-01 - 2020-01 Sr Machine Learning / Data Science Engineer North Bay Solutions

I worked on implementing Computer Vision algorithms to enhance video surveillance systems. My expertise includes Deep Learning techniques such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks

(GANs) for image processing and recognition. I have experience working with TensorFlow, Apache MXNet, Caffe, and Theano for model development and deployment.

In the Insurance sector, I applied Bayesian Methods and supervised learning models to predict claim amounts and assess risk profiles. My work involved the use of Time Series Analysis for forecasting and Transfer Learning for improved model performance. I employed Python, R, SAS, and RapidMiner for data analysis and model building, as well as Tableau and Power BI for data visualization.

2015-01 - 2018-01 Sr Software Engineer

Self Employed

Computer Vision: My experience as a Machine Learning Engineer in the Computer Vision domain includes developing object detection and recognition systems using Deep Learning techniques such as CNNs and GANs. I have worked with popular frameworks such as TensorFlow, Apache MXNet, and Caffe, and have utilized Python, R, MATLAB, and Theano for model development and implementation.

Green Energy: In the Green Energy industry, I focused on optimizing energy consumption using Reinforcement Learning algorithms like Policy Gradient Methods. My work involved the use of Time Series Analysis for forecasting energy Education

demand and Anomaly Detection for identifying system faults. I employed Python, R, MATLAB, KNIME, and H2O for data analysis and model building, while utilizing visualization tools like D3.js and Plotly for data presentation. 2013-01 - 2015-01 Software Engineer

CureMD

For the Lifestyle industry, I worked on creating personalized recommendation systems using unsupervised learning models and Natural Language Processing. My expertise includes implementing K-Means clustering and PCA for dimensionality reduction, as well as utilizing Python, TensorFlow, Pandas, NumPy, and Jupyter for data analysis and model development. I have also used visualization tools such as ggplot2, Seaborn, and Power BI to present insights and results.

2009-01 - 2013-05 BS: CS

Lancaster University

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