Data Analyst
Transforming raw data into actionable insights. Specialized in SQL, Python, and Power BI to drive data-driven decisions.
EPAM Systems
Currently enrolled in the DAE (Data Analytics Engineering) trainee program, learning advanced data engineering concepts and best practices.
Najot Ta'lim
Completed 7-month intensive data analytics program covering SQL, Python, Excel, Power BI, and practical data analysis projects.
Ajou University in Tashkent
3rd year student pursuing a degree in Electrical and Computer Engineering, building a strong foundation in technical problem-solving.
Company lacked visibility into profit margins across products and regions, making it difficult to identify underperforming areas.
50K+ transactions, Revenue, COGS, Operating Expenses, Net Income (2021-2024)
Gross Profit Margin, Net Profit Margin, Operating Margin, YoY Growth, Revenue by Category
Identified 15% cost reduction opportunity in logistics. Q4 showed 23% higher margins than Q1-Q3 average.
Optimize supply chain in low-margin regions. Focus marketing budget on high-performing Q4 period.
Online store experienced high cart abandonment rates and needed to understand customer purchase patterns.
100K+ orders, Customer data, Product catalog, Transaction logs (2022-2024)
Conversion Rate, AOV, Cart Abandonment Rate, Customer Lifetime Value, Repeat Purchase Rate
Mobile users had 40% higher abandonment. Peak sales occurred Tuesday-Thursday. Electronics category drove 45% of revenue.
Improve mobile checkout UX. Launch targeted promotions on peak days. Expand electronics inventory.
Sales team lacked real-time visibility into performance metrics and regional sales trends.
200K+ sales records, 50 sales reps, 5 regions, Product hierarchy (2020-2024)
Total Revenue, Sales Growth %, Quota Attainment, Win Rate, Average Deal Size, Sales Cycle Length
Top 20% of reps generated 65% of revenue. North region underperformed by 18%. Deal size increased with longer cycles.
Implement mentorship program pairing top performers with struggling reps. Reallocate resources to high-potential territories.
Marketing team used one-size-fits-all approach, resulting in low engagement and high customer churn.
75K customers, Purchase history, Demographics, Behavioral data, RFM metrics
RFM Score, Customer Segments, Churn Rate by Segment, CLV by Segment, Engagement Rate
Identified 5 distinct segments: Champions (12%), Loyal (18%), At-Risk (25%), New (20%), Dormant (25%). Champions had 8x higher CLV.
Create personalized campaigns per segment. Implement win-back program for At-Risk. VIP program for Champions.
Company spent $500K annually on marketing but couldn't measure ROI or identify best-performing channels.
30 campaigns, Multi-channel data (Email, Social, PPC, Display), Conversion tracking
ROAS, CAC, Conversion Rate, CTR, CPM, Campaign ROI, Attribution Model
Email had 4.2x ROAS (highest). Display ads had negative ROI. Social media drove awareness but low direct conversions.
Increase email budget by 40%. Cut display advertising. Use social for top-of-funnel only with retargeting.
Warehouse experienced inefficiencies in order fulfillment, leading to delayed shipments and customer complaints.
150K+ orders, Warehouse logs, Inventory levels, Shipping data, Staff schedules
Order Fulfillment Time, On-Time Delivery %, Inventory Turnover, Pick Accuracy, Labor Productivity
Monday mornings had 3x longer fulfillment times. Zone B had 15% lower pick accuracy. Stockouts caused 22% of delays.
Add staff on Monday mornings. Retrain Zone B team. Implement automated reorder points for high-velocity items.
I'm currently looking for data analyst opportunities. Feel free to reach out if you have a position that matches my skills!