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Data Analytics & Business Intelligence
Turning raw operational data into decision ready intelligence with reproducible analysis workflows.
Multidisciplinary technologist and data strategist bridging programming, statistical modeling, financial analysis, and business development to build high-leverage solutions.
// 01 - About

Over time I have developed a solid set of hard and soft skills that have enabled me to take on diverse roles and responsibilities. Beginning with a foundation in technology and later branching into business, finance, and media, I have had the opportunity to understand the unique demands of each industry and what it takes to succeed in them.
Motivation keeps me driven, adaptability helps me adjust, and focus enhances productivity. Programming is a vital skill in today's digital world, communication allows ideas to be shared effectively, and sales is essential to every business. With a basis in these skills I am confident in my ability to add value to any team I join.
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Motivation
Sustained drive across long horizon technical work.
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Adaptability
Fast context switching between tech, finance and media.
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Focus
Deep work discipline that protects analytical accuracy.
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Programming
R, SQL, Python and modern web engineering.
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Communication
Translating quantitative findings into clear decisions.
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Sales
Commercial instinct applied to research and product.
Download Curriculum Vitae (PDF)
A full breakdown of experience, technical stack, statistical methods, research output and business development work. Recruiters, research leads and venture partners can review everything in one file.
PDF · 95 KB · v2026.08 · Updated August 2026
Focus
Data + Finance
Format
1 page PDF
Status
Open to roles
// 02 - Services
Twelve capability blocks spanning analysis, engineering, research and commercial strategy.
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Turning raw operational data into decision ready intelligence with reproducible analysis workflows.
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Dashboards that expose the signal: clear encodings, honest scales, zero decorative noise.
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Hypothesis testing, confidence intervals, ANOVA and inference built on validated assumptions.
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Linear and logistic regression, variable selection, transformations and prediction intervals.
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Production interfaces and internal tools engineered for speed, clarity and maintainability.
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Partnership pipelines, market sizing and go to market structures grounded in quantitative evidence.
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Valuation frameworks, cost structure teardowns and investment memos backed by primary data.
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End to end research: question framing, methodology, diagnostics and defensible conclusions.
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Complex quantitative findings translated for executive and non technical audiences.
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Technical copy, documentation and content systems that stay precise under scrutiny.
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Media workflow design from capture to distribution, informed by audience analytics.
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Scheduled, reproducible reporting that removes manual assembly from recurring analysis.
// 03 - Portfolio
In-depth statistics, data science and mathematical research. Open any deck in the interactive viewer.
25 SlidesProject 01
When does a price stop being a price and start being an extraction?
An analytical breakdown of predatory retail markup structures, inflationary cost shifting and consumer price elasticity models, built as a teaching module with live cross store comparisons.
21 SlidesProject 02
Which measurable race factors actually decide a Grand Prix outcome?
Econometric and statistical analysis evaluating how telemetry, qualifying position, pit strategy and tire degradation influence lap times and victory probability across 225 Grand Prix winners.
// 04 - Blog
Analytical writing on data science practice, pricing economics, motorsport analytics and technology.

A model with a strong R squared can still be wrong in every way that matters. Residual plots, leverage and normality tests come first.
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Selling price minus cost is simple arithmetic. What complicates it is unit pricing games, segmented pricing and inflationary cost shifting.
Read Article
Three predictors explain two thirds of the variance in Grand Prix duration. Everything else in the dataset was mostly noise.
Read Article
Recurring analysis should never be hand assembled. But the interpretation layer is exactly where humans still earn their seat.
Read Article
Communication is a technical skill. If a stakeholder cannot restate your finding accurately, the analysis has not shipped.
Read Article
Most dashboards are decoration with a refresh rate. A useful one answers a specific recurring question in under ten seconds.
Read Article// Open to collaboration
Analysis, modeling and engineering support for teams that need decisions backed by defensible numbers.
// 05 - Contact
Direct coordinates and a structured intake form. Every inquiry is read personally.