Saniya Parveez
Data Scientist
Bengaluru, Karnataka, United States
Summary
🤩
Rockstar🎓
Top SchoolSaniya Parveez is a data scientist and analytics engineer with 7 years of hands-on experience building production-grade ETL pipelines, data models, and automated workflows primarily on AWS, Redshift, and Airflow. At Red Hat she progressed from trainee to senior data engineer, designing data pipeline architectures, driving database migrations to S3/Redshift, and deploying Dockerized solutions with CI/CD. She has a strong analytics toolkit—Python, Pandas, NumPy, dbt, Tableau—and a track record of reducing ETL errors and boosting efficiency by 30% while improving customer retention through targeted email triggers. An active contributor to practical AI/ML tutorials on GitHub, she publishes approachable guides on PCA, decision trees, and Colab workflows that bridge theory and application. With a B.E. in Computer Science and multiple publications in machine learning and optimization, she combines research rigor with operational discipline to turn data into measurable business impact.
7 years of coding experience
3 years of employment as a software developer
B.E - Bachelor of Engineering, Computer Science, B.E - Bachelor of Engineering, Computer Science at Visvesvaraya Technological University
English
Github Skills (17)
python10
data-science10
matplotlib10
scikit10
pandas10
machine-learning10
pca10
scikit-learn10
decision-tree10
linear-algebra10
data-analysis10
user-manual9
google-colab9
deep-learning8
neural-network6
Programming languages (2)
Github contributions (5)
towardsai/tutorials
Oct 2020 - Nov 2020
AI-related tutorials. Access any of them for free → https://towardsai.net/editorial
Role in this project:
Data Scientist
Data ScientistContributions:8 commits, 1 PR, 7 pushes in 25 days
Contributions summary:Saniya contributed significantly to the repository by implementing and demonstrating various data science and machine learning techniques. The commits include code related to linear algebra, principal component analysis (PCA), correlation and covariance matrices, and decision tree classification. Additionally, the user provided tutorials and examples using Python, Pandas, Scikit-learn, and Matplotlib, demonstrating practical applications in the context of machine learning and data analysis. The user also provided tutorials for using Google Colab with relevant packages.
pythonmathematicssentiment-analysiseditorialapl
saniyaparveez/saniyaparveez.github.io
Sep 2020 - Oct 2020
Contributions:24 pushes, 1 branch in 19 days
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