Start with the work the role requires. Clear decision writing and basic commercial analysis are useful foundations; add SQL when you need repeatable data access and AI when you can verify its output. Collecting tool names is not the same as building competence.
Build the foundation around a decision
Choose a simple business question: why are qualified enquiries failing to become customer calls? Define each stage, identify missing data and calculate the conversion rates. Write a recommendation that separates evidence, assumptions and next steps.
The WEF’s 2025 skills outlook places importance on both analytical thinking and technological literacy. For an individual candidate, the useful response is to combine reasoning with tools—not assume a software certificate proves business judgment.
Spreadsheets before elaborate dashboards
Learn to clean a dataset, use lookups, create a pivot table and explain a formula. Check for duplicates, missing values and inconsistent date formats. A beautiful dashboard built on incorrectly defined metrics can make decisions worse.
Create a small synthetic funnel and write down the definitions beside it. Another person should be able to reproduce the answer without asking you what each column means.
Add SQL when the work justifies it
SQL becomes useful when the role regularly needs structured data from company systems. Start with filtering, grouping and joins. Pay particular attention to table grain: joining one customer to many events can inflate totals if you do not account for it.
Practise with public or synthetic datasets. In a company, use approved read-only access and check results against a known report. Never query sensitive employee or customer data just to make a portfolio project more impressive.
Use AI as an assistant you can audit
AI can help draft a query, structure research or produce a first version of a memo. You still need to validate calculations, verify sources and understand what the query actually does. Keep confidential data out of unapproved tools.
Build a portfolio example that includes the initial question, your checks and an error you corrected. Showing how you rejected a plausible but wrong answer is often more persuasive than showing a flawless-looking final output.
A suggested four-week learning sequence
- Week one: create a metric dictionary and spreadsheet analysis.
- Week two: write a one-page decision memo from the findings.
- Week three: reproduce the analysis in SQL if relevant.
- Week four: add an AI-assisted step and document validation.
Finish with a handover, not just a demo. A candidate who makes work understandable and repeatable is demonstrating the operating discipline that a Founder’s Office role needs.
FounderSide’s practical editorial guidance for startup hiring and operations. Examples and assessment frameworks are illustrative, not reported client outcomes. Adapt them to your company’s stage and needs.
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