I'm a former Senior Officer at Energypac Electronics, where I specialized in Banking, Treasury, Internal Audit, and Costing. After years of working at the intersection of financial operations and analytical problem-solving, I made a deliberate transition into Data Science and Analytics — bringing a practitioner's eye for what numbers actually mean in business contexts.

My academic foundation in Economics (MSS & BSS) combined with an ongoing CMA qualification informs how I approach data: not just as patterns to visualize, but as evidence for decisions. I'm currently building toward Predictive Analytics and ML-driven decision systems that can model complex economic and behavioral patterns.

Languages & Libraries

BI & Visualization

Platforms & Tools

Python · Selenium · Pandas · NumPy · Tableau

Scraped and analyzed 7,197 product listings from two of Bangladesh's leading online grocery platforms — Chaldal and Shwapno — to surface market-level pricing and promotional intelligence.

Key findings:

• Shwapno has a larger catalogue; Chaldal leads in Non-Food SKUs (except Personal Care)
• Pran and ACI dominate as top named brands on both platforms
• Discounts cluster in Non-Food — Personal Care leads in free product availability
• Small, Standard-priced products receive the most offers; Large products are nearly excluded
• Personal Care shows the widest price gap between platforms

Deliverable: An interactive Tableau dashboard revealing cross-platform trends and pricing gaps for consumer and market strategy use.

𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 · 𝐏𝐨𝐰𝐞𝐫 BI

Started with raw HR data and built a complete analytics workflow from database to dashboard:

• 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 → Structured data storage with 5 relational tables.
• 𝐒𝐐𝐋 → 12 business-question-driven queries (attrition, compensation, performance, diversity, attendance).
• 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 → Interactive HR dashboard

Pandas · 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐒𝐐𝐋 · Power BI

• End-to-end data analytics project built on a retail sales dataset.
• Simulated a real-world business intelligence pipeline — starting from raw data cleaning with pandas, structured storage in PostgreSQL, and finally an interactive multi-page dashboard in Power BI.

HTML · CSS · JavaScript · WordPress · Hostinger

A collection of responsive websites built from scratch and via WordPress — currently being migrated to GitHub repositories.

• Custom-built static sites using vanilla HTML & CSS
• WordPress-based sites with custom layout & design using plugins
• Few projects hosted on Hostinger

🚧 WordPress Repositories uploading — links coming soon.           

Senior Officer — Finance & Accounts | Apr 2024 – Jun 2025

• Banking & Treasury: Prepared cash flow statements to monitor liquidity and optimize fund allocation. Disbursed salaries, prepared bank advices, RTGS instructions, and managed payment files. Performed bank reconciliations between SAP and bank statements for transactional accuracy.
• Financial Reporting: Assisted CFO with key financial reports for board meetings — AR aging, inventory aging, budget vs. actuals.
• Costing: Determined MRP and DP pricing through margin analysis and market trend evaluation. Assisted in stock clearance, gross profit margin, and selling price determination.

HR Operations Support (additional responsibility) | Jul 2024 – Jun 2025

• Processed monthly OT, allowances, incentives, and deductions for payroll management.
• Prepared HR documents: appointment, promotion, transfer, and separation letters.

Officer — Internal Audit & Compliance | May 2023 – Mar 2024

• Audited PR, PO, CS, VAT challans, and GRNs, reducing errors by 80%.
• Audited OT, TA, DA, and incentive policies — resulting in 20% cost savings.
• Introduced training initiatives that enhanced accounting accuracy by 85%.
• Supported payroll audits, credit recovery, and performed inventory audits at factory and central store.

• ERP System: SAP
• Financial Analysis: Microsoft Excel (Advanced), Financial Modeling, Data Reconciliation
• Reporting: PowerPoint, Dashboards, Word

• Advanced Machine Learning algorithms (regression, classification, clustering)
• Statistical modeling for forecasting and inference
• Feature engineering and model evaluation pipelines

I'm particularly drawn to problems where economic reasoning meets messy real-world data — whether that's consumer pricing behavior, financial risk signals, or operational inefficiencies hiding in ledger entries. If data tells a story about how people, markets, or institutions actually behave, I want to build the model that reads it.

Developed by @bithiNath ⚡