Abdalla Abker, Meng Email and Phone Number

• Collaborate on ETL tasks (Extract, Transform, Load), perform downloading data from API, applying data cleansing techniques in Python and Apache Hop, and Loading consolidated data in Azure data lakes.• Executing tasks in sequence arranged into (DAGs) utilizing Apache Airflow Scheduler• Build, test, and collaborate codes using Version Control tools – Git, and packaging codes in Docker• Apply agile methodologies CI/CD for robust, scalable, and end-to-end automation

• Create ML model to detect patients’ motion, utilizing IoT data that isgenerated from sensor fusion devices, to predict whether a patient is makingprogress, by utilizing the power of Microsoft Azure ML.• Compile, clean, and manipulate data from sensor fusion IoT devices• Perform Data wrangling, Exploratory Data Analysis, and Statistical Analysison incoming data• Apply K-means, DBSCAN, and t-SNE unsupervised clustering techniqueson the sensor fusion data to find out and detect various motions• Utilize supervised learning modeling techniques with Scikit-learn such asLogistic Regression, SVM, K-NN, Decision Tree, Random Forest, XGBoost,and Naive Bayes algorithms to classify patients' motions.• Perform Machine Learning model training, hyperparameters tuning, andmodel evaluation• Prepare and present technical reports to management

Hands-on training in Data Science & Machine Learning Bootcamp using Python as the main tool to conduct studies.- Performed data wrangling, cleaning, explanatory data analysis, and descriptive statistics- Visualized data using Matplotlib, Seaborn, plotly and cufflinks libraries, in addition to Tableau- Performed Loading and Preprocessing using Scikit-learn- Applied Classification models such as Logistic Regression, SVM, K-NN, Decision tree, Random Forest, and Naive Bayes Classification, Regression models, to mention few, Logistic Regression, SVM, K-NN, Decision tree, Random Forest, and Clustering models such as K-Means Clustering and Hierarchical Clustering- Familiarized with Artificial Neural Networks and Deep Learning concepts: ANN, CNN, RNN using Keras and Tensorflow- Built models using state-of-the-art automated machine learning tools and validate the results based on statistical metrics - Familiarized with SQL databases and Cloud

Mainly, I was a member of Schlumberger team, assigned to log shallow wells up north in KEARL, an Oil Sand Project owned by Imperial Oil Limited, for 2018-2019 Winter Project.● Successfully evaluated reservoir petrophysical characteristics in wireline logging jobs for conventional and unconventional open hole wells ● Confidently led a team of 2-3 employees of various jobs ● Built professional and friendly relations with clients on field sites ● Run Acoustic, Nuclear, Resistivity & SP, Caliper Logging Tools

• Maintained an excellent safe work practice for both open and cased hole wells• Achieved Schlumberger Fixed Step professional training: OFS-1, PEPTEC-0, PEPTEC-1, OFS-2 (PEPTEC-Wireline Technical Course, OFS-Management Course)• Successfully evaluated reservoir petro-physical characteristics in wireline logging jobs for both open and cased hole• Obtained fluid sample and pressure points for open hole wells• Successfully run depth correlation jobs for cased hole wells• Evaluated cement bond integrity for cased hole wells• Confidently led a team of 4-5 employees of various jobs• Built a professional and friendly relations with clients on field sites

• Analyzed reservoir fluid rocks and fluids to design the recovery methods and predict reservoir performance.• Monitored and forecast reservoir performance and the suitable recovery methods.• Designed and select artificial lift equipment.• Observing well site wells’ production, Field Processing Facilities operation, and installation of ESP pumps.• Observed well site drilling operation.