About me
Marketing Data Analyst building the measurement and decision systems behind where marketing budget goes. The domain knowledge comes from having run the campaigns, not just modelled them.
- Name
- Rafael Braga-Kribitz
- Role
- Marketing Data Analyst
- Based in
- Seiersberg, Greater Graz, Austria
Background
I worked as a Marketing Data Scientist at BioSynCare, on the questions every marketing team eventually faces: what actually worked, and where the next euro should go. My way into a question is to take the system apart, find the mechanism behind the number, test the explanation before trusting it, then build whatever is needed to make the answer repeatable.
1 Paraguay elected Mario Abdo Benítez in April 2018, by a margin of 3.7 percentage points (46.43% against 42.73%, official count of the electoral tribunal, TSJE).That habit predates the job title. My career is in marketing, and the technical work was never a detour from it; it was how the marketing got done. When a question outgrew the available tools, I built the tool: scrapers where the data was not offered, automation where a process did not deserve manual hours, text and sentiment analysis on what customers said, ad copy generated programmatically and tested in experiments. The same pattern ran through the startups I founded and through my work as part of the campaign that helped elect Mario Abdo Benítez president of Paraguay.11 Paraguay elected Mario Abdo Benítez in April 2018, by a margin of 3.7 percentage points (46.43% against 42.73%, official count of the electoral tribunal, TSJE).
The tendency was probably there all along: my mother is a mathematician, my father an engineer, and treating problems as systems to be taken apart was the default at home. Marketing is where that habit proved its worth. Marketing Data Science is the same combination, working under its proper name.
Expertise & Approach
The approach is consistent across domains: frame the decision first, estimate honestly with uncertainty attached, then optimise against the metric that actually matters. The toolbox below serves that sequence. Not the other way around.
What the marketing career adds is the domain half of the job: knowing how campaigns are planned, run, and reported on. That is where most measurement goes wrong before any statistics are involved.
Skills
- Domains
- Marketing Data Science · Quantitative Marketing · Marketing Science
- Methods
- Causal Inference · Bayesian Methods · Forecasting · Optimisation
- Toolbox
- Python · PyMC · DuckDB · dbt · DVC · Git · SQL · Spark
- Practice
- Experiment design · Decision systems · Measurement & attribution
CV
Experience
| Dates | Role | Organisation |
|---|---|---|
| 2023–2026 | Marketing Data Scientist | BioSynCare – marketing measurement, experimentation, and decision support |
Contact
Open to Marketing Data Analyst roles – and to second opinions on hard measurement problems.