Max Santana

Data Analyst, Business Intelligence & Strategic Foresight Specialist

Hi there 👋, welcome to my portfolio

👨‍💻 A brief about me

• 📊 I use rigorous data analytics and strategic intelligence to turn complex, messy data into actionable business intelligence 📈 and long-term strategic decisions 🎯.
• 🌐 International relations graduate specialised in data analysis and strategic foresight 🔮 — transforming chaos to clarity ✨ and moving from uncertainty to opportunities 💡.
• 🚀 Currently advancing into Data analysis and Applied AI & Data Science Solutions 🤖.
• ⚡ Fun fact: I love Dachshunds; hoping to fulfil my dream to have one or two little wieners to join me while I code

📬 Connect & Collaborate

• 📧 Email: msantana.r@outlook.com
• 📱 Phone: +52 220 501 4335

🛠️ Technical Stack & Capabilities

• Data Analytics & BI: SQL, Python (pandas, numpy, seaborn, matplotlib), R (tidyverse, ggplot2), Power BI, Tableau, RStudio, Jupyter, Google Colab.

• Core Technical Focus: End-to-end data pipelines, cleaning raw datasets (100k+ records), data modeling, statistical forecasting, and dynamic dashboard design.

• Business & Strategy: Business question-oriented analysis, KPI framework design, executive communication (CFI consulting framework), decision support, and stakeholder alignment, Project Management, Conflict resolution

• Strategic Foresight & Risk Analysis: Strategy facilitation, scenario planning, horizon scanning, geopolitical risk analysis, actor mapping, long-term scenario building under uncertainty, HUMINT, and OSINT.

• Social Science & Research: Qualitative research (expert consultations, interviews, surveys, focus groups), public policy analysis, and political economy.

📁 Projects

Territorial Profitability Analysis — Adventure Works

🎯 Objective

Determine where to allocate marketing spend for maximum ROI across territories by analyzing revenue, costs, and marketing investment.

🔍 See full case study

🔧 What I Did

1. Schema Integration — Joined 6 tables (sales, products, categories, territories, campaigns) using clave_territorio and clave_producto
2. Data Cleaning — Calculated ingreso_total and costo_total per order, handled NULLs with COALESCE
3. KPI Calculation — Aggregated revenue, gross profit, margin %, and ROI % by territory
4. Validation — Reconciled totals across joins, confirmed no data anomalies

🛠️ Technologies

SQL (JOINs, GROUP BY, aggregations, COALESCE, NULLIF) | Relational database with 6 tables | Data validation & QA

📊 Results

Country	Revenue	Margin %	ROI %
🇺🇸 USA	$3.35M	43.4%	75.8% ⭐
🇦🇺 Australia	$2.53M	41.7%	49.2%
🇬🇧 UK	$1.19M	42.7%	22.1%
🇩🇪 Germany	$1.07M	42.9%	20.3%
🇫🇷 France	$0.92M	42.9%	17.9%
🇨🇦 Canada	$0.71M	44.8%	17.4%

💡 Key Insight

USA leads with 75.8% ROI on $1.92M spend. Australia (49.2% ROI) punches above weight. UK underperforms with only 22.1% ROI despite $2.3M investment. All margins healthy (41–45%), but ROI divergence driven by marketing spend efficiency.

Recommendation: Reallocate ~$500K from underperforming EU/CA markets to USA/Australia for 30–40% ROI improvement.

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ConnectaTel — Customer Behavior & Usage Segmentation

🎯 Objective

Determine which customers drive the most value for a Latin American telecom by cleaning usage data, resolving data-quality issues, and segmenting customers by usage intensity and age.

🔍 See full case study

🔧 What I Did

1. Data Quality Diagnosis — Detected sentinel values (age = -999, city = ‘?’), impossible dates (40 records dated 2026), and confirmed duration/length nulls were Missing At Random by usage type
2. Cleaning — Replaced sentinels, standardized dates, and preserved MAR nulls as meaningful signal rather than imputing them away
3. Feature Engineering — Aggregated 40,000 usage records into a per-user profile (messages, calls, call minutes)
4. Outlier & Segmentation Analysis — Used IQR and Z-scores to identify power users, then segmented all 4,000 customers by usage (Low/Medium/High use) and age

🛠️ Technologies

Python (pandas, numpy) | seaborn, matplotlib | IQR & Z-score outlier detection | Rule-based segmentation | Google Colab

📊 Results

Medium use is the largest usage segment; a consistent minority of 21–47 users per metric are high-volume “power users” retained as an upsell target rather than cleaned away as noise. Basic plan dominates every segment, including the heaviest users, pointing to under-monetized power users.

💡 Key Insight

Outliers were the opportunity, not the noise. The heaviest 21–47 users per usage metric were kept — not trimmed — since they represent ConnectaTel’s clearest upsell segment.

Recommendation: Design an ultra-premium tier for these power users and target Medium use customers, already the largest segment, for migration incentives toward High use/Premium.

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Urban Mobility & Economic Productivity — Latin America

🎯 Objective

Determine where a development bank should invest in transport infrastructure by analyzing how urban mobility (congestion, delay) relates to economic productivity (GDP per capita, unemployment) across 15 Latin American cities.

🔍 See full case study

• Data Integration — Merged TomTom traffic records with OECD city economic indicators using city and year keys
• • Data Cleaning — Standardized column formats, parsed European numeric formatting, converted timestamps, filtered to 2024
• Aggregation — Grouped traffic records by city to calculate mean delay, congestion, and travel-time metrics per city-year
• Analysis — Computed a congestion-to-productivity ratio and ran correlation analysis across GDP, congestion, unemployment, and population

🛠️ Technologies

Python (pandas, numpy) | seaborn, matplotlib | Data wrangling & correlation analysis | Jupyter Notebook

📊 Results

City	GDP/Capita	Ratio	Profile
🇨🇴 Bogotá	$11,442	0.100	Highest urgency ⭐
🇵🇪 Lima	$13,472	0.078	Highest urgency ⭐
🇲🇽 Mexico City	$21,111	0.134	High-scale congestion
🇧🇷 São Paulo	$14,703	0.118	High-scale congestion
🇧🇷 Brasília	$16,251	0.006	Efficient benchmark
🇺🇾 Montevideo	$26,176	0.002	Efficient benchmark

💡 Key Insight

The correlation matrix suggests that traffic jam is driven mainly by population size (r = 0.88), not GDP per capita (r = 0.28) as initially expected. Bogotá and Lima combine high traffic friction with lower economic output — the clearest case for investment. Mexico City and São Paulo show the highest absolute congestion, but it’s scale-driven, not inefficiency. Montevideo and Brasília stand out as efficient benchmarks.

Recommendation: Prioritize transit investment in Bogotá and Lima for the highest expected economic return per dollar spent.

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Índice de Riesgos de Corrupción (IRC) — IMCO

🎯 Objective

Identify corruption risk in public procurement across 260+ Mexican federal institutions by evaluating compliance with three principles: competition, transparency, and rule of law.

🔍 See full case study

🔧 What I Did

1. Research Support — Supported the IRC project in a research capacity, focused on interpreting procurement risk findings
2. Results Presentation — Helped translate analytical results into clear insights and presentation materials for public policy audiences
3. Stakeholder Reporting — Contributed to progress reporting to USAID as project funder

🛠️ Technologies

R (data analysis) | Tableau (interactive dashboard) | Public policy & governance research

📊 Results

💡 Key Insight

Between 2018 and 2020, corruption risk increased in 147 of 247 federal institutions (59%), driven by weak competition, low transparency, and non-compliance. The tool was adopted as a reference in Mexican public policy debates on transparency and institutional integrity.

Future of Aid 2040 — IARAN

🎯 Objective

Explore four plausible futures for the global humanitarian aid system by 2040, translating foresight into strategy for organizations navigating funding cuts, politicization, and systemic uncertainty.

🔍 See full case study

🧭 Scenario Matrix

Four scenarios mapped across two axes: network cooperation vs. survival of the fittest, and multipolar blocs vs. empires and conflict.

🔧 What I Did

1. Trend Research — Led documentary research on global trends (incl. AI) as lead analyst for LATAM
2. Stakeholder Facilitation — Facilitated consultations in Mexico and coordinated multiple stakeholders (donors, strategic partners, consulted organizations) within project governance
3. Multi-Phase Delivery — Contributed across the project’s three phases: foundations (Causal Layered Analysis), scenarios, and transformation pathways

🛠️ Key Methodologies

Strategic Foresight | Horizon Scanning | Causal Layered Analysis (CLA) | Scenario Building | Stakeholder Alignment

📊 Results

Metric	Value
Consultations	50+
Contributors	877
From the Global South	77%
From local NGOs / CSOs	44%
With lived crisis experience	~40%

💡 Key Insight

Synthesizing 877+ voices from 50+ organizations — most from the Global South — into four scenario frameworks and an organizational toolkit for humanitarian resilience under high uncertainty.

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Geopolitical Risk & Horizon Scanning

🎯 Objective

Actor mapping and strategic intelligence under conditions of high uncertainty for executive decision-making.

🔍 See full case study

🛠️ Key Methodologies

OSINT | Strategic Intelligence | Qualitative Risk Analysis

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Tierra Incógnita: The Future of the Creative Economy — Nuevo León

🎯 Objective

Build a foresight-based framework to turn uncertainty into opportunity for Nuevo León’s creative and cultural industries, facing accelerated digital disruption, AI integration, and post-pandemic pressure.

🔍 See full case study

🔧 What I Did

1. Workshop Design & Facilitation — Designed and led strategic foresight workshops with creative-sector actors and key decision-makers
2. Policy Translation — Translated the foresight exercise into an actionable public policy framework (Logical Framework Approach)

🛠️ Key Methodologies

Horizon Scanning | Scenario Building | Strategic Facilitation | Logical Framework Approach

📊 Deliverables

Horizon scanning report for the creative economy

4 future scenarios for Nuevo León’s creative industries

Public policy framework (Logical Framework Approach)

Published strategic policy report