Shajahan Sayyed
Professional Summary
Data Analyst with 1+ years of experience delivering data validation, ETL automation, and dashboarding using Python, SQL and cloud data platforms. Skilled in building reproducible data pipelines with Pandas and Alteryx, designing Snowflake-backed datasets for reporting, and authoring complex SQL (CTEs, window functions) for reconciliation and root-cause analysis. Experienced creating interactive Tableau and Power BI dashboards to surface KPIs and operational trends for healthcare and e-commerce use cases. Applied statistical EDA and anomaly detection to improve data quality and support fraud-detection research projects using Scikit-learn, SMOTE and ensemble models. Comfortable deploying analytical applications and models to AWS and documenting data transformations for auditability and stakeholder consumption.
Technical Skills
Work Experience
- Analyzed and reconciled healthcare datasets in Snowflake using SQL (CTEs and window functions) to produce validated reporting tables and support ad-hoc investigations.
- Built Python data pipelines with Pandas to clean, standardize, and transform claims and operational feeds for downstream analytics.
- Automated ETL workflows using Alteryx and Python to integrate multi-source clinical and operational data and reduce manual preparation steps.
- Developed interactive dashboards in Tableau and Power BI to track KPIs, operational trends, and exception alerts for business stakeholders.
- Wrote complex SQL queries to extract, aggregate and reconcile transaction-level records to enable root-cause analysis and audit-ready datasets.
- Performed exploratory data analysis and anomaly detection in Python (Pandas, NumPy) to identify data-quality issues and define remediation rules.
- Translated ambiguous business rules into repeatable ETL logic and documented transformations to ensure metric consistency across reports.
- Authored Power Query transformations and Excel-based validation checks to standardize supplier CSV feeds before ingestion.
- Mentored students in Python programming and database concepts, designing step-by-step examples to clarify core algorithms and SQL patterns.
- Guided peers through data-analysis project design, recommending EDA approaches and feature-engineering strategies using Pandas.
- Reviewed and debugged student codebases in Jupyter Notebook, improving reliability of submitted assignments and reproducibility.
- Prepared sample datasets and walkthroughs demonstrating data-cleaning techniques and SQL query optimization patterns.
- Coached project teams on presentation structure and documentation to strengthen technical delivery and stakeholder communication.
- Organized peer-learning sessions focused on practical use of Git, SQL querying, and data-visualization best practices.
Projects
- Built an end-to-end analytics pipeline ingesting 100k+ Olist e-commerce records from AWS S3 via boto3, cleaning and transforming data with Pandas and loading processed tables into MySQL for querying.
- Analyzed delivery performance across regions and identified a correlation between delivery delays and declining review scores, informing prioritization of logistics improvements.
- Designed a 4-chart Tableau dashboard visualizing revenue trends, regional performance, delivery delay impact, and customer segments to inform product and operations decisions.
- Developed a content-based movie recommender using TF-IDF and CountVectorizer with cosine similarity in Scikit-learn to return top-N similar titles based on metadata.
- Performed feature engineering and text preprocessing with Pandas and integrated the model into a lightweight web app for real-time recommendations.
- Deployed the application to AWS to enable scalable online access and reduced local compute dependency for end users.
- Trained and compared supervised models (Logistic Regression, Random Forest, XGBoost) for binary fraud classification, evaluating precision, recall, F1 and ROC-AUC.
- Addressed severe class imbalance using SMOTE and applied feature selection to identify transaction patterns relevant to fraud risk.
- Packaged and deployed the selected model to AWS for scalable inference and integrated predictions into offline analysis pipelines.
Education
Certifications
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