Professional Summary
Detail-oriented Data Analyst with 5+ years of progressive experience in banking operations, sales analytics, and financial reporting. Currently working at Lumbini Bikas Bank's CEO Office, where I transform raw financial data into actionable insights that directly support C-level strategic decisions.
Proficient in Power BI, Power Query, Advanced Excel, with hands-on experience building dashboards, tracking KPIs, and automating reporting workflows. Currently completing intensive live training in Python, SQL, and Pandas for advanced data analytics.
Proven ability to collaborate cross-functionally, identify performance gaps, and deliver data-driven recommendations that improve operational efficiency. Seeking a dedicated Data Analyst role to fully leverage my analytical expertise and business acumen.
Technical Skills
Professional Experience
- Designed and delivered 12+ monthly executive dashboards in Power BI tracking branch performance, loan portfolio quality, and deposit growth across 50+ branches, directly utilized by CEO and Board for quarterly strategic planning.
- Analyzed financial data across NPAs, CAR, credit-deposit ratios, and profitability metrics, identifying 5 underperforming locations and recommending corrective actions.
- Automated monthly financial reporting using Power Query and Excel macros, reducing report preparation time from 40+ hours to 8 hours (80% time savings).
- Orchestrated cross-departmental data collection workflows involving Credit, Operations, Finance, HR, and IT teams (30+ stakeholders), standardizing reporting templates and improving data accuracy.
- Conducted 15+ research projects on regulatory compliance, competitor analysis, and market expansion, delivering executive-ready presentations for board-level meetings.
- Performed monthly variance analysis on budgeted vs. actual performance across revenue, expenses, and loan disbursements, providing early-warning insights.
- Analyzed 12 months of sales and customer data using Advanced Excel to identify revenue trends, seasonal patterns, and performance gaps across product lines and customer segments.
- Built Excel-based KPI dashboards tracking customer acquisition cost (CAC), retention rate, revenue per customer, and monthly sales growth, reducing reporting time by ~60%.
- Cleaned and validated 10,000+ customer and sales records, implementing data quality checks that improved record accuracy to 95%+.
- Collaborated with sales, operations, and finance teams to standardize 5+ recurring reports, reducing turnaround time by 2-3 days.
- Led team handling 100+ daily customer interactions, achieving 95%+ satisfaction through efficient resolution and escalation management.
- Analyzed inquiry patterns to identify recurring issues, implementing process improvements that reduced resolution time by ~20%.
- Maintained accurate customer and transaction records supporting monthly reporting and account reconciliation.
- Managed examination registration database for 500+ monthly IELTS candidates, ensuring data accuracy and timely application processing.
- Handled 40+ daily inquiries from diverse international clientele, developing strong communication skills in high-volume professional environment.
- Processed candidate documents and payment records with 99%+ accuracy, maintaining data integrity for operational reporting.
- Delivered structured lessons to 100+ students, developing presentation and communication skills applicable to data storytelling and stakeholder reporting.
- Tracked student performance data to identify learning gaps and tailor interventions, improving pass rates by ~15-20%.
Professional Development
Curriculum Coverage:
Projects & Portfolio
Built interactive Power BI dashboard analyzing performance metrics across 50+ bank branches including deposits, loans, NPAs, and profitability. Integrated data from Finacle core banking system using Power Query.
Analyzed 12 months of sales data to identify seasonal patterns, top revenue drivers, and customer segments. Created automated Excel dashboards tracking CAC, retention, and conversion rates.
Exploratory Data Analysis project using Python and Pandas. Applied data cleaning, transformation, and visualization techniques. Statistical analysis and insight generation.