Data Analyst · BI Analyst · Financial Analyst

I turn scattered economic
and transaction data into
decisions worth trusting.

Built an early-warning sovereign risk model across 183 countries using IMF & World Bank data, engineered 10 analytical SQL investigations, and designed 6 Tableau dashboards for macroeconomic risk monitoring — backed by an MBA in Finance & Analytics and 2.5+ years across MIS reporting, risk modelling, and business intelligence.

Pavan Kumar Magandi

Pavan Kumar Magandi

4 End-to-End Projects 23K+ Records Processed SQL · Python · Excel · Power BI · Tableau · R Research Contribution — MCX India
01 — About

From process floors to risk models

My path here wasn't a straight line. I moved through process engineering, then commodity research, then an MBA — and somewhere in that journey realized where I was actually strongest: I'm comfortable with numbers, and I like turning them into an answer someone can act on. So I built the technical side deliberately — SQL, Python, Tableau, Power BI, Excel — to back that finance judgment with tools that could actually process the data.

MBA Finance & Analytics, IBS Hyderabad
Open to Data Analyst, BI Analyst, Financial Analyst, Credit/Risk Analyst, and similar roles
Structures and cleans raw datasets using SQL and Power Query.
Translates business questions into precise, efficient SQL queries.
Builds interactive Tableau dashboards for data exploration and decision support.
I can take a dataset from raw, messy, and unreliable to validated and decision-ready — and present it through an interactive Tableau dashboard that stakeholders can actually explore and use to make decisions.
02 — Selected Work

Projects built to be interrogated, not just admired

FLAGSHIP PROJECT
PILLAR — SOVEREIGN & MACRO RISK

Global Sovereign Risk Dashboard

PostgreSQL · Tableau · Excel · Python · IMF WEO & World Bank GEM data

183 Countries 2018–2026 IMF WEO World Bank GEM PostgreSQL Tableau

An end-to-end early-warning system across 183 countries. Raw IMF and World Bank feeds were cleaned, deduplicated, and reconciled into a 4-pillar, 8-indicator weighted "Safe Score" — modelled loosely on S&P / Moody's / Fitch sovereign methodology — then backtested against three real crises to confirm it would have flagged trouble early.

Outcome — Backtesting showed the Safe Score would have flagged deteriorating conditions in Sri Lanka, Ghana, and Pakistan ahead of their 2022 debt crises, giving the model real evidence it can surface early warning signals rather than confirm a crisis after the fact.

  • 10 PostgreSQL analytical queries using CTEs, window functions (LAG/LEAD/RANK), and multi-table joins across four risk pillars
  • Caught and corrected a silent DISTINCT ON / ORDER BY logic error that had been substituting IMF forecasts for observed actuals
  • 6 interactive Tableau dashboards — 20+ worksheets, 150+ calculated fields — with diverging risk maps and parameter-driven country drill-down
23,149 → 1,627 raw records deduplicated into verified country-year observations
5 data-integrity defects diagnosed and fixed at source, not patched over
3 real sovereign crises used to backtest the model — Sri Lanka, Ghana, Pakistan
01 — Command center

Main Dashboard

The overview screen: 183 countries on a diverging risk map, live counts of Safe, Watchlist, and High-Risk economies, a ranked score table, and a debt-doubling-time leaderboard running from the fastest-eroding economies to the safest.

Business QuestionWhich countries need attention right now?

InsightA single map and ranked table surfaces Watchlist and High-Risk economies at a glance, instead of requiring a country-by-country review.

DecisionUse the debt-doubling-time leaderboard to prioritize which economies get a deeper drill-down first.

02 — Trade resilience

Executive Summary

Export growth by income group laid against each safe and unsafe country's current-account trend, surfacing where a quietly deteriorating trade balance is eating into a country's safety margin before it shows up elsewhere.

Business QuestionIs a country's trade position quietly weakening before its overall risk score shows it?

InsightExport growth and current-account trends often move before the composite Safe Score does.

DecisionTreat a worsening current-account trend as an early flag, not just a supporting data point.

03 — Proof it works

Model Validation

Safe Score trajectories for Sri Lanka, Ghana, and Pakistan visibly deteriorating ahead of their real 2022 debt crises, benchmarked against stable economies like the US, China, and India.

Business QuestionWould this model actually have caught a real crisis in advance?

InsightReserve adequacy and fiscal indicators collapsed in the Safe Score before the 2022 debt crises became public knowledge.

DecisionPrioritize reserve adequacy and fiscal-balance indicators as leading, not lagging, signals in the monitoring workflow.

04 — Single-country drill-down

Solvency & Fiscal

Every underlying indicator's z-score plotted year over year — fiscal balance, GDP growth, government debt, inflation, reserve adequacy — so it's clear exactly which lever is pushing a country's score up or down.

Business QuestionFor a specific country, what exactly is driving its risk score?

InsightAggregate scores hide which individual indicator is responsible for a change; the z-score breakdown does not.

DecisionDrill into the single indicator behind a score movement instead of reacting to the headline number alone.

05 — Trade resilience by income tier

Income & Trade

Export growth broken out by income group — high, upper-middle, lower-middle, low — layered against each safe and unsafe country's current-account trajectory, showing where trade momentum is diverging by income tier and which countries' account positions are stabilizing or worsening.

Business QuestionDoes risk exposure differ by income tier, or is it evenly spread?

InsightTrade momentum diverges meaningfully by income group, so a single global benchmark can mask tier-specific weakness.

DecisionCompare a country against peers in its own income tier rather than the full 183-country pool.

06 — The leaderboard

Risk Composition & Rankings

The top 10 safest and least-safe economies by Safe Score, paired with year-over-year change, so a worsening trend stands out even before a country crosses into high-risk territory.

Business QuestionWho's trending toward risk, not just who's already there?

InsightPairing the rank with year-over-year change catches a country sliding toward high-risk before it crosses the threshold.

DecisionFlag countries with the sharpest negative year-over-year change for monitoring, even if their absolute score still looks safe.

PILLAR — COMMERCIAL ANALYTICS

Northwind Traders Sales Analytics

Power BI · Excel · SQL · EDA

Power BI Sales Analysis Customer Segmentation Inventory & Logistics

A six-page Power BI suite built on the Northwind wholesale-distribution dataset, covering sales performance, customer segmentation, employee productivity, product and supplier analysis, and order/logistics tracking — designed to give stakeholders one place to answer their recurring business questions.

01 — Command center

Main Dashboard

One-screen command center pairing headline KPIs — customers, revenue, orders, avg. shipping days — with a quarterly revenue-by-category flow and a top-selling-products ranking, so overall health can be checked without opening a separate page.

02 — Customer intelligence

Customer Analysis

Segments the customer base by country/city and by job title, and tracks YoY/MoM order and revenue growth — surfaces which regions and buyer roles are actually driving orders, useful for prioritising account outreach.

03 — Logistics benchmarking

Order Analysis

Benchmarks the three shipping carriers on cost and average delivery days by country, alongside order-value bucket distribution — flags exactly where delivery delays and freight cost are concentrated.

04 — Team productivity

Employee Analysis

Tracks orders handled and revenue generated per employee, title, and territory — surfaces which reps and regions are carrying the order volume and where coverage is thin.

05 — Margin visibility

Product Analysis

Separates units sold from actual revenue contribution by category and price band, using a revenue treemap alongside a top-products ranking — catches products that look strong by volume but weak on margin.

06 — Supply concentration

Supplier Analysis

Maps supplier country and category contribution against revenue, helping identify supply-concentration risk where a single supplier or country dominates a category.

PILLAR — BEHAVIOURAL ANALYTICS

Movie Rental Analytics

Power BI · SQL · EDA

Power BI SQL Customer Behaviour Revenue & Store Ops

Power BI dashboard and SQL-driven exploratory analysis on the Sakila DVD rental dataset, spanning customer behaviour and retention, film & inventory utilisation, staff performance, and revenue trends across stores.

01 — Command center

Overview Dashboard

Combines revenue trend, store-wise KPIs, and customer value segments on one page — quickly shows whether growth is coming from more rentals or from higher revenue per rental.

02 — Customer value

Customer Analysis

Layers customer value segments (low/mid/high) onto geography and revenue-vs-count trends, making both high-value clusters and thin markets visible at a glance.

03 — Inventory utilisation

Film Inventory

Cross-references inventory volume against rental rate and category popularity — surfaces over-stocked categories that rent infrequently versus under-stocked categories in high demand.

04 — Staff performance

Staff Performance

Compares transaction counts and revenue handled per staff member and store — useful for staffing decisions and performance-review conversations.

05 — Revenue trends

Revenue Trends

Breaks revenue down by month and store to expose seasonal peaks and troughs sitting behind the headline growth number.

06 — Store benchmarking

Store Operations

Compares the two stores head-to-head on rentals, revenue, and inventory turnover — flags which store is under- or over-performing relative to its inventory size.

PILLAR — PORTFOLIO & RISK

Portfolio Optimisation & Financial Risk Management

Excel · CAPM · Regression · Monte Carlo · VaR

CAPM Regression (Beta Estimation) Efficient Frontier Sharpe Ratio Value at Risk Monte Carlo Simulation

Constructed an optimal 10-asset portfolio from a 50-asset universe using CAPM and Efficient Frontier analysis to evaluate risk-return trade-offs and identify the best-fit allocation, then stress-tested it with Value at Risk and Monte Carlo simulation across market-crash, liquidity-crisis, and bull-market scenarios.

  • Ran linear regression of each asset's historical returns against market returns to estimate beta, then applied the Capital Asset Pricing Model (CAPM) to derive each asset's expected return relative to its systematic risk
  • Built the full covariance/correlation matrix across the 50-asset universe and mapped the Efficient Frontier, selecting the 10-asset combination that maximised the Sharpe ratio for the best risk-adjusted return
  • Quantified downside exposure with historical and parametric Value at Risk (VaR), then ran Monte Carlo simulations to stress-test the portfolio under market-crash, liquidity-crisis, and bull-market conditions and set risk-adjusted breach thresholds
03 — Toolkit

What I build with

Languages & Tools

Python (Pandas, NumPy, Scikit-learn) PostgreSQL / MySQL SQL — CTEs, Window Functions Advanced Excel & Power Query Power BI Tableau R

Analytics

EDA & Statistical Modelling Financial Modelling Time-Series & Forecasting Data Validation & Cleaning KPI & Performance Reporting

Risk & Finance

Sovereign & Macro Risk Credit Risk Scenario & Stress Testing Portfolio Optimisation AML & Transaction Monitoring Regulatory Reporting
04 — Experience

Where I've delivered impact

Mar 2025 – Jun 2025

Management Trainee — Sourcing & Analytics

Xanadu Realty, Bengaluru

  • Built an Excel-based lead-mapping and deduplication solution across 5+ channel-partner networks, surfacing 300+ high-intent prospects and cutting estimated cost-per-qualified-lead by ~25%
  • Developed an Excel dashboard for lead-to-walk-in performance tracking, enabling KPI reporting, funnel analysis, and data-driven resource allocation across acquisition channels
  • Ran campaign measurement, ROI analysis, and lead-source trend analysis to support conversion tracking, campaign optimisation, and budget prioritisation
Apr 2024 – May 2024

Research & Analytics Intern — Financial Analytics

MCX India, Hyderabad

  • Ran time-series and volatility analysis on 15+ years of MCX base-metals data, identifying market risk patterns for commodity price forecasting
  • Built R-based visualisations to quantify downside risk and support evidence-based investment and hedging decisions
  • Acknowledged for research on commodity price risk and hedging strategies, published on MCX India's research portal and reaching institutional investors internationally; also assessed ESG/SDG compliance of global smelting firms
Aug 2019 – Mar 2022

Associate Process Testing Engineer & Team Lead

TCL–POTPL, Tirupati

  • Generated SOP-compliant MIS reports and process documentation while tracking production output and defect trends, improving reporting accuracy and audit readiness
  • Implemented an Excel-based inventory monitoring system that maintained optimal stock levels and eliminated production interruptions
  • Led a cross-functional testing team to a mass-production milestone 3 weeks ahead of schedule despite COVID-19 disruption, coordinating across 3 departments with zero critical defects at handover

Education

2023 – 2025

MBA — Finance & Analytics

IBS Hyderabad, IFHE University

Coursework: Business Analytics, Financial Modelling, Hypothesis Testing, Statistical Inference — CGPA 7.78/10

2015 – 2019

B.Tech — Electronics & Communication Engineering

Andhra University College of Engineering

CGPA 7.64/10

Languages

English — Professional Hindi — Native Telugu — Mother Tongue
05 — Certifications & Recognitions

Credentials and milestones along the way

Expected Sep 2026

Data Science Program — Accio

Certificate by E&ICT IIT Guwahati

SQL (PostgreSQL), Python for Data Analytics, Advanced Excel, Power BI — complete. Currently pursuing Machine Learning and GenAI.

MCX India Research Studies Portal

Published Research Contributor

MCX–ICFAI Business School Study

Acknowledged contributor to "Hedging of Price Risks in Base Metals," a published study on commodity price risk and hedging strategy.

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National-level exams & olympiads

Academic Distinctions

CAT 2022 — 92.93 percentile · NMAT 2022–23 — 242 · National distinctions in AMTI, AIMEd, and SIMO.

Pavan Kumar Magandi
06 — Contact

Let's build something meaningful with data.

Open to Data Analyst, BI Analyst, Financial Analyst, Credit/Risk Analyst, and similar roles.