Open to Data / Business Analyst roles in Australia and Chile
Hi, I'm Rodolfo.
I turn data into
Data and Business Analyst from Chile, now in Australia. Hands-on analytics experience across financial services (pensions), telecom and heavy equipment, from SQL pipelines to the stakeholders who act on them. Industrial engineer with postgraduate training in Data Science and AI, with a strong interest in banking, investments and fintech.
Critical-alert model, held-out machines
SELECT duration_bucket,
SAFE_DIVIDE(ported_5d, contacted)
AS port_out_rate
FROM churn_reporting.calls
GROUP BY 1 ORDER BY 2 DESC;
A bit about me
I'm an industrial engineer from Santiago who fell in love with turning messy data into clear decisions. I started in financial services (pension and retirement analytics at Principal), moved into heavy-equipment telematics, and most recently worked on churn analytics in telecommunications.
I enjoy the whole path: SQL pipelines and statistics at the bottom, models and experiments in the middle, and dashboards that people actually use at the top. I like projects where a number changes what a team does tomorrow, such as who to call first, which KPI to trust, or which machine to service.
Right now I'm based in Perth, Australia, looking for my next data analyst, business analyst or data science role, ideally in financial services, banking or fintech.
Finance is a long-term interest, not just a sector: I manage my own diversified long-term portfolio, and I'm exploring how automation and AI can help small businesses through a prototype called Lumora.
Outside work: football (soccer), motocross and snowboarding. I like fast feedback loops, on and off the screen.
Experience
Click a role to see what I did.
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- Built an automated BigQuery pipeline that detects competitor calls and measures 5-day port-out risk across ~700k daily calls, with Looker Studio reporting for management.
- Identified segments with 4-6x higher churn probability, enabling retention outreach to ~600 customers per day.
- Segmented customers by churn risk and ran A/B tests (control vs treatment) to measure retention campaigns; found conversion differences of up to ~35% between segments.
- Automated churn, behaviour and competitor reports, cutting analysis and execution time by up to ~65%.
- Redesigned a low-quality sales KPI (67% precision, 9% recall) with a decision tree, proposing rules with 91.5% precision or 33% recall.
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- Led a corporate telematics data integration project, improving internal coordination and collaboration with customers.
- Analysed machine telemetry to find opportunities in failure prediction and after-sales service optimisation.
- Produced KPI reports to support management decisions, and managed stakeholders by providing support and training.
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- Analysed pension and retirement data to generate financial insights supporting retirement planning.
- Automated and optimised reporting with VBA and SQL, reducing delivery times by 85.3%.
- Worked with financial analysts and advisors to improve report quality and data-driven decisions.
Projects
Work projects and personal ones. Click a card for the story and the results.
WorkReal results
Competitor Contact & Churn Impact Analytics
Daily BigQuery pipeline linking competitor calls to customers and measuring port-out within 5 days. Found a segment with 4-6x higher risk.
WorkReal resultsSynthetic demo
Silent-Sales KPI Redesign
Validated a sales-quality KPI (67% precision, 9% recall) and used a decision tree to propose two new rules, with a runnable demo.
WorkSynthetic data
Machinery Failure Telemetry Analysis
Data quality, EDA and an ML model (logistic regression vs random forest) to prioritise critical machine alerts. Fully reproducible.
PersonalTechnical assessment
School Dropout Dashboard (Chile)
332,766 students left subsidised private schools in 2023-24. R processing plus an interactive Power BI dashboard.
Personal
Twitch Streamers EDA
What do the top 1,000 Twitch channels do? Data-driven advice for a friend who wants to grow as a Spanish-speaking streamer.
Personal
E-commerce Data Cleaning
21 messy files and 82,105 rows turned into 71,480 unique products, with every cleaning decision documented.
Skills
Filter by area, and hover or tap a skill to see where I used it.
- SQL (BigQuery)
- Python (pandas, scikit-learn)
- R (tidyverse)
- Excel and VBA
- Git
- Google Cloud Platform
- Web scraping
- Power BI
- Looker Studio
- Tableau
- A/B testing
- Statistical analysis
- Machine learning
- Deep learning
- KPI design
- Business analysis
- Financial services and investing
- Stakeholder management
- Process automation
- AI automation (LLMs)
Hover or tap a skill to see where I used it.
Education


