Creating tools and data products for Microsoft Account teams to help them boost customer success and experience with Microsoft Azure.Generating ML models and insights about cloud revenue and consumption to better understand customer experience and their consumption patterns.Handling A to Z on Data Science process;Creating POC data science work in Dev environment. Iterating and pivoting based stakeholders demand and need. Then, deploying value-proven codes to production using Azure architecture (Synapse & Azure Data Factory). Supporting dashboarding teams to build reports on top of our data products.Very close to Microsoft business unit (MCAPS, The Microsoft Customer and Partner Solutions) to answer their demands from data perspective. Specialized in Azure revenue and consumption patterns.

• As program coordinator, building a software program at Lantern.• Teaching SQL and databases. Teaching data manipulation and data definition languages. • Teaching Tableau and data visualization to help building the bridge between business and analytics.

As a member of the advanced analytics and engineering team helped stakeholders with diverse backgrounds in the company to tackle business problems from data perspectives.• Developed anomaly detection model, additive model, to notify business owners for unprecedented decline transactions in the network. Provided supporting insights for root cause analysis.• Developed k-means clustering, to detect use cases for e-transfer transactions, used for customer segmentation and marketing purposes.• Developed an automatic analytical tool for the product team to identify underperforming sectors in the debit market to actively respond to market projections (using regression models).• Productionalized proof of concept prototypes into automated analytical products. Created the entire data pipeline on a cloud-based environment (AWS).• Leveraged big data tools such as Spark and Hive on Hadoop environment to enhance speed and efficiency of our analytical tools.

Teaching Programming for Data Science to graduate student at the Faculty of Information, St. George campus.

• Applied statistical analysis and machine learning algorithms on large datasets from automotive companies to predict failures in their mechanical systems• Developed machine learning algorithms including recurrent and convolutional neural network for classifying faulty and healthy transmissions (using TensorFlow, H2o, scikit-learn, and Keras)• Led group of three people in two internal projects on benchmarking Acerta’s algorithms using end of line engine’s data• Worked on integration of various modules for developing an API to standardize structure of data pipeline (using Python object oriented programming, TensorFlow, R, and Git)• Investigated root cause analysis of failed transmissions and conducted feature engineering to develop domain knowledge based models (utilizing combination of mechanical knowledge and statistical analysis)• Worked on Amazon Web Service Cloud (AWS) platform such as Linux on Elastic Compute Cloud (EC2) and Simple Storage Service (S3)

• Developed a computational fluid dynamics (CFD) modeling software written in C++ for oil sands purification, master’s thesis - supervised by Prof. Markus Bussmann• Developed numerical modeling of multivariable Navier-Stokes equations• Added new features to a software to run transient modeling of the mechanical phenomenon• Taught Python programming: held tutorials and lab sessions for undergraduate students • Taught Calculus and Numerical Methods: held tutorials for a class of 140 students

• Python Programming (APS106): held tutorials and lab sessions for first year undergraduate students • Calculus and Numerical Methods (CHE221): Held tutorials for class of 140 students• Advanced Fluid Mechanics (MIE1201H): Marked and composed questions

• Programmed with computer numerical control (CNC) machine• Performed Solid Modeling for presenting products to customers• Operated CNC machine, produced 150 mechanical parts per day as a part of a manufacturing line