Arsalan Ahmed

• Improved system accuracy and performance using Grafana by analyzing user activity data and system-driven results. • Created cross-validated ML model result insights to enhance the ML team's continuous improvement. • Streamlined data migration pipelines to optimize efficiency. • Integrated review team schedules from Google Calendar to AWS Timestream to generate team insights for performance and capacity metrics, leading to increased productivity.

• Developed ETL pipelines for health records data using Airflow, Ascend.io, Spark, Kubernetes, and Git CI/CD tools to improve data processing efficiency and accuracy. • Implemented distributed computing frameworks to achieve a 50% reduction in pipeline computation time, resulting in faster data analysis and insights. • Established a scalable and efficient data processing system, leading to faster insights and increased productivity of the team.

Improved contentstudio.io search using the Knowledge Graph approach & Elasticsearch, leading to 30% more user engagement Analyzed social media engagement using high-performing Airflow ETL pipelines, improving marketing campaigns & revenue Innovative social media caption generation led to 20% more user engagement Reduced system costs & improved reliability with infrastructure projects including High Availability database cluster management Implemented error logging & alert management systems.

• Spearheaded development of a cost optimization AI engine & recommendation system for Austin Texas gov & Dubai RTA, reducing public congestion by 20% • Developed moving vehicle advertisements for Ford, increasing attraction & target marketing • Played critical role in COVID-19 Tracker project, providing vital information & garnering large number of visitors during pandemic • Contributed to the Accident Prediction project, improving the existing system's accuracy from 60% to 75%.

Led development of recommendation system for job and candidate matching using machine learning on Indeed data Developed ML model for accurate job recommendations based on user search queries & preferences Analyzed job market trends to improve system & inform business decisions Presented findings to stakeholders & potential investors, generating interest in future partnerships.

• Work on development PackageX, a WeWork last-yard delivery solution that improved delivery efficiency • Evaluated current Textual Information Extraction algorithms, identifying areas for improvement • Led creation of data augmentation pipeline for unstructured data labeling, reducing labeling time by 40% • Built deep learning NER system for courier image information extraction, achieving 95% accuracy and improving data processing efficiency.

• Executed POCs for the banking sector, resulting in the development of new strategies. • Implemented a Cash Replenishment System, resulting in a 20% reduction in operational costs. Developed models using pytorch and tensorflow framworks. • Developed and implemented a customer 360-based Anomaly Detection system for broadband data, leading to a reduction in broadband theft. • Developed a License Plate Recognition System, which led to a 30% increase in recognition for the Islamabad Traffic Police.

• Led startup in the development of a Smart Home Solution/Facial Recognition-based Security System, achieving successful proofs-of-concept. • Smart Home Solution led to a 30% reduction in energy costs and improved security in tests. • Contributed to the development of a Facial Recognition-based Security System, leading to a significant reduction in security incidents in tests. • Represented the company at 4YFN Barcelona and Mobile World Congress in March 2017, increasing visibility and industry recognition.