Shajahan Sayyed
Professional Summary
Data Analyst with 0 years of experience applying SQL, Python, ETL and BI tools to transform operational data into stakeholder-ready insights. M.S. Data Science candidate with internship experience building automated ETL pipelines, interactive Tableau dashboards, and predictive models to reduce manual effort and inform business decisions.
Technical Skills
Work Experience
- Queried and analyzed manufacturing and operational datasets using SQL (multi-table JOINs, GROUP BY, window functions) and Python (Pandas) to surface process gaps and performance drivers; analysis contributed to a 20% improvement in campaign performance insights used by management.
- Built and maintained 5+ interactive Tableau dashboards for plant management and business-unit stakeholders, reducing manual reporting time by 30% and improving operational visibility for daily decision-making.
- Automated recurring reporting workflows with Python (Pandas, NumPy), eliminating 8–10 hours of manual work per week and increasing report freshness for stakeholders.
- Implemented ETL steps for ingestion, cleaning, and validation of multi-source operational data to standardize formats and improve downstream reporting reliability across pipelines.
- Documented data definitions, transformation logic, and dashboard requirements; presented findings and prioritized recommendations that informed operational and scheduling adjustments.
- Worked cross-functionally with operations and business teams to translate technical results into stakeholder-ready visualizations and action items, accelerating adoption of data-driven process improvements.
Projects
- Designed an end-to-end analytics pipeline on 100k+ e-commerce records: ingested CSVs from AWS S3 using boto3, cleaned and transformed data with Pandas, and loaded processed tables into MySQL for analysis.
- Used SQL aggregations and window functions to identify top revenue product categories and map regional performance; found a category that contributed 10.6% of total revenue across 27 states.
- Analyzed delivery delay impact on customer satisfaction, linking orders delayed >7 days with average review score drops from 4.5 to 3.1; recommended prioritized logistics fixes to reduce churn.
- Processed 50,000+ transactional records to extract revenue trends, seasonality, and regional performance gaps using SQL and Python.
- Developed KPI-driven dashboards in Tableau and Power BI visualizing revenue growth, margin, and regional sales, reducing ad-hoc reporting requests by 40%.
- Standardized data collection and validation steps and delivered documentation for management use in monthly business reviews.
- Segmented 80,000+ customer records by recency, frequency, and monetary metrics using SQL and K-Means, identifying high-risk churn cohorts and priority retention targets.
- Built visualizations in Tableau to communicate cohort behavior and recommended targeted campaigns projected to reduce churn by ~15%.
- Cleaned and reconciled CRM fields using Pandas to ensure segmentation accuracy and repeatability across analyses.
- Built classification models (Random Forest, Logistic Regression) on 15,000+ HR records to predict attrition with 87% accuracy; performed feature engineering to surface top predictors (tenure, compensation, engagement).
- Used SHAP values to explain model drivers and created a Power BI dashboard showing attrition risk by department and role level for HR decision-making.
- Delivered recommendations that informed workforce planning scenarios and projected turnover mitigation strategies.
- Designed and implemented an automated ETL pipeline to ingest sales data from CSVs, databases, and S3; transformed and validated 100k+ records weekly into a centralized reporting store.
- Scheduled pipeline jobs and automated data quality checks, eliminating 12+ hours of manual preparation per week and reducing reporting errors by 35%.
- Built downstream Tableau dashboards to provide near real-time sales KPIs and documented the pipeline architecture for engineering and analytics teams.
Education
Certifications
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