Muhammad Ibrahim Email and Phone Number

• Utilized Microsoft SQL Server to query and analyze large-scale insurance datasets, extracting key metrics to drive data-driven decision-making processes• Developed interactive Power BI dashboards by integrating real-time sensor data• Analyzed complex data using statistical techniques to drive meaningful change on KPIs and stakeholder-established objectives• Built Extract, Transform, and Load (ETL) pipelines for data consolidation and preparation using Azure Databricks, Azure DevOps and Azure Data Factory (ADF)• Automated the data pipeline for processing real-time sensor data using Python, streamlining data collection and reporting for enhanced operational efficiency

• Working within a team to build a lower body exoskeleton that easily holds high amounts of weight. • Working with Arduino IDE to gather data which is further used to optimize the exoskeleton.

• Utilized MySQL to query and manage web traffic data, leading to noticeable improvements in databaseperformance and data retrieval times for web analytics reporting• Developed interactive Tableau dashboards to visualize key web metrics, such as site traffic, bouncerates, and user behavior patterns, enabling stakeholders to make data-driven decisions• Employed advanced Excel functions like VLOOKUP, INDEX/MATCH and PivotTables to clean andanalyze web performance data, improving data accuracy and reporting efficiency• Leveraged Python with the Pandas and NumPy libraries to automate data preprocessing, manipulation,and statistical analysis of large datasets, enhancing reporting speed and accuracy

• Led a winning team in developing and presenting product take-back solutions for Spin Master, following extensive research and analysis to align with sustainability goals and optimize returns.• Demonstrated strong problem-solving and consulting skills, delivering impactful presentations to the client, resulting in our solution being chosen as the most effective for Spin Master

• Led a team to design and implement CNN-based neural networks for object detection for robotics engineering, resulting in successful builds for competition• Deployed YOLOv4 for object detection, improving detection speed by 50% and achieving a precision rate of 92% in diverse environments • Utilized Python and deep learning frameworks such as TensorFlow and PyTorch to develop AI models, reducing model training time by 33%• Designated the testing of different ML algorithms between the team members to test, report and compare the accuracies, leading to finalizing the best performing algorithm