Reha Hotmail

Spearheaded the development and migration of data and pipelines, enhancing our data processing capabilities and fostering a more agile environment. This facilitated the creation of insightful Machine Learning and AI models, leveraging user SEO and media experience data for optimized content and improved user engagement metrics.
• Data Processing and Orchestration: Designed and oversaw data stacks, meticulously upholding stringent data governance and quality protocols while seamlessly integrating diverse data sources. Implemented processes to optimize data preparation within the Hive and Presto Hadoop environments, facilitating seamless utilization by Machine Learning algorithms. Developed and new data flows strategies, oversaw potential of This significantly enhanced data processing and capabilities, a more agile and data-driven environment. Utilizing experience in orchestrating data flows and implementing Machine Learning models, led the initiative to leverage user SEO and media experience data for content optimization. By harnessing the power of advanced analytics and machine learning algorithms, I spearheaded efforts to identify high-engagement content and enhance user engagement metrics. Leveraging data stored in data warehouses such as Hive and
Presto, I orchestrated the loading ETL processes to ensure seamless access to relevant data for analysis. Through strategic data modeling and algorithm development, enabled our team patterns in user behavior, identifying content that attracted higher views and engagement. This data-driven approach empowered our content optimization strategies, resulting in improved user engagement and enhanced content performance. leading Data Integration and Analysis initiatives, orchestrated the consolidation of diverse datasets sourced from disparate departments into centralized pipeline. logic Apache Kafka, ETL
ensure smooth data flow and interoperability across our ecosystem. Through meticulous design and implementation of integration workflows, I facilitated the harmonization of varied data sources, enabling comprehensive analysis and insightful decision-making. This approach not only streamlined data accessibility but also deepened our understanding of organizational data dynamics, empowering informed strategic initiatives.

Data pipelines were optimized for efficiency and machine learning models were improved, empowering stakeholders with dynamic data exploration and informed decision-making. . Data Pipelines Performance Tuning: Involved optimizing data processing workflows and maximizing operational efficiency. Utilizing Scala and Spark, developed and managed data flow diagrams, ensuring streamlined data movement and efficient task execution. By integrating Airflow for job scheduling, orchestrated the timely execution of data pipeline tasks, minimizing processing delays and maximizing resource utilization. . Machine Learning Model Improvements: In the realm of Machine Learning Model Improvements, my focus has been on optimizing data pipelines and enhancing machine learning algorithms. Leveraging Scala and Spark, meticulously crafted data flow and data lake diagrams, ensuring efficient data management and preparation for machine learning tasks. Under Airflow orchestration, implemented the seamless execution of machine learning workflows, facilitating timely model updates and enhancements, significantly improving the performance and accuracy of machine learning models, driving actionable insights and business value. . Dashboarding: Created denormalized versions of data, employing robust data modeling techniques to provide business users with more insightful outputs. Leveraging Tableau's powerful visualization capabilities, I transformed complex datasets into intuitive and interactive dashboards, enabling stakeholders to gain actionable insights at a glance. By crafting denormalized data structures and optimizing data flows, I facilitated seamless integration with Tableau, ensuring that business users have access to accurate and up-to-date information for informed decision-making. Through these efforts, empowered stakeholders to explore data dynamically, uncover trends, and drive strategic initiatives with confidence.

Creation of infrastructure, including EMR clusters, Spark, Kafka, and Hadoop environments, with a focus on maintaining robust security measures, spearheading initiatives to automate cluster creation using Terraform scripts, enhancing operational efficiency and reinforcing data security.
• Infrastructure creation and provision: Architectured the seamless establishment of EMR clusters, Spark and Kafka environments, and Hadoop clusters, employing rigorous security protocols to ensure the integrity of data operations. Moreover, I led initiatives to automate cluster creation processes using Terraform scripts, enhancing operational efficiency and reducing manual intervention. This comprehensive approach not only facilitated the rapid deployment of scalable infrastructure but also reinforced data security measures, laying a robust foundation for streamlined data processing workflows. . Data processing: Devised and implemented efficient data pipelines, leveraging various technologies and methodologies to ensure smooth data flow and effective processing. Utilizing a combination of tools and techniques, I orchestrated data processing workflows, optimized resource allocation, and enhanced data integrity and reliability.

• Designed, developed, and productized machine learning models, tailored to optimize key business outcomes for the client. Utilized various techniques such as supervised learning (classification and regression), unsupervised learning (clustering, anomaly detection), and reinforcement learning to provide predictive insights. Models integrated into production using containerized environments for scalability and reliability.
• Developed a recommendation engine using collaborative filtering and content-based filtering techniques. Leveraged matrix factorization (SVD) and deep learning-based approaches (Neural Collaborative Filtering) to improve accuracy. The deployment was automated via AWS Lambda and API Gateway for real-time recommendations, significantly enhancing customer engagement metrics (CTR, purchase rate).
• Spearheaded the development of an anomaly detection system using a combination of Isolation Forest, One-Class SVM, and autoencoders. models amount of money in potential fraud-related losses.
• Collaborated with data engineering teams to build scalable data pipelines on Airflow, handling more than Terra bytes of data. Techniques such as bucketing, partitioning, and caching were applied to optimize the training pipeline, reducing data access latency and decreasing training time. This enabled rapid model experimentation and faster time to market.
• Utilized AWS SageMaker to automate model training and deployment workflows, with Docker containers ensuring isolation and scalability of deployed models. Implemented monitoring using Amazon CloudWatch and SageMaker Model Monitor to reduce model drift and maintain high model accuracy in production.
• Adopted MLOps best practices by integrating CI/CD pipelines (GitLab CI, Jenkins) for model deployment. Automated testing and validation (unit tests for model features, performance tests for inference speeds), ensuring code quality and reducing time-to-market. Used MLflow for tracking experiments and monitoring model versions.
• Built and deployed supervised learning models (random forest, XGBoost, and neural networks) to optimize inventory management, leveraging time series forecasting techniques like ARIMA and Prophet. Achieved a reduction in operational costs through improved demand forecasting and inventory restocking strategies.
• Fine-tuned a pre-trained ResNet architecture using transfer learning for an image classification task. Techniques such as learning rate schedulers, data augmentation, and dropout layers were applied to improve model generalization. Achieved high accuracy rate, significantly outperforming custom models developed from scratch.
• Ensured the proper randomization of datasets to avoid selection biases and confounding variables. Applied techniques such as stratified sampling, data augmentation, and bootstrapping to ensure the robustness of model predictions across diverse data distributions.

For iTunes department, development of an end-to-end ETL pipeline, efficiently processing external data into our organization's warehouses. Through optimized workflows and disaster recovery measures, built an environment ensuring seamless operations and provided stakeholders with valuable guidance on database utilization. 4. Data Pipeline Creation: Orchestrated the design and implementation of an end-to- end ETL pipeline to ingest and process data from external sources into the GBI division's data warehouses. Utilized Apache Airflow to schedule and monitor job flows, ensuring smooth pipeline operations in production.
. Pipeline Optimization :_ Leveraged Hadoop and Spark clusters to optimize data processing workflows, employing multilevel partitioning in Hive for efficient data fetching. Implemented rule evaluation processes in Spark to apply business rules and store processed data in databases.
• Data Modeling: Developed data models in data warehouse for storing transformed data and facilitating normalization and denormalization for reporting purposes. Created sub-pipelines for extracting metadata from RDBMS db and managing configuration settings for streamlined pipeline operations across different data sources.
• Disaster Recovery: Designed and implemented end-to-end disaster recovery processes between clusters and utilized Terraform for defining cloud infrastructure as code. Implemented disaster recovery cycles effectively to minimize downtime and ensure business continuity. Provided guidance to stakeholders on utilizing database management systems effectively through collaboration and documentation of standard methodologies.