technical expertise
As a data and ML professional, I apply intelligent, data-driven approaches to solve complex problems, optimize processes, and uncover opportunities for innovation across diverse domains.
01 ds & DA
Data Science & Analysis: As an expert in SQL, Python, and advanced data visualization frameworks, I have developed end-to-end analytical solutions that transform complex, unstructured data into meaningful insights. My work involves building predictive and statistical models, designing automated data workflows, and integrating analytical outputs into business and technical systems. Through thorough experimentation and data-driven evaluation, I help uncover patterns, forecast trends, and support intelligent decision-making across diverse domains.
02 ml & Ai
Machine Learning Model Development:With proficiency in TensorFlow, Scikit-learn, and PyTorch among other libraries, I’ve designed and trained a wide range of machine learning models, including supervised and unsupervised systems, deep neural networks, and reinforcement learning frameworks. My experience also covers natural language processing, computer vision, feature engineering, model optimization, deployment, and MLOps practices to ensure reproducibility, scalability, and efficient lifecycle management across diverse domains.
03 de
Data Engineering & Management: With a strong foundation in MySQL, PostgreSQL, SQLite, NoSQL, Snowflake, Hadoop, Kafka, Spark, MongoDB, and other technologies, I designed and implemented scalable data pipelines and ETL workflows that integrated with cloud-based environments. I engineered and managed large-scale databases and data systems to support efficient storage, retrieval, and analysis of big data, improving scalability, accessibility, and overall performance in data-driven operations.
04 cloud
Cloud Technologies: I possess extensive experience with AWS, Azure, and GCP. This includes developing cloud-based AI solutions, setting up and managing cloud databases, and leveraging cloud technologies to build scalable data and machine learning applications. My work involved architecting end-to-end cloud environments, integrating storage and compute services, and deploying containerized ML workflows. These implementations supported the growth of data-intensive systems and enabled efficient management of increasing data volumes, computational workloads, and real-time analytics needs.
