23 tasks, each one witnessed by the sources that watched the job — and behind every one, a prompt you can use tonight.
You spend days observing real users in their settings: homes, work sites, or online communities, watching how they use products and taking detailed notes. That maps to tasks like breaking systems into components, defining data requirements, and analyzing data to identify trends and patterns.
You convert observations into deliverables: field notes, affinity maps, interview transcripts, and presentations for management. You use tools like Google Docs for notes and reports, GitHub for versioning research scripts or code, and sometimes SPSS for statistical checks.
Start with Google Docs for writing reports and sharing findings, GitHub for storing analysis code or reproducible notebooks, and IBM SPSS Statistics if your team runs formal quantitative tests. These are explicitly named in the job evidence.
Also learn simple recording gear, Excel or similar for quick tables, and at least one qualitative-analysis method (affinity mapping, coding). The tasks include preparing management reports, defining data requirements, and validating information, so practical tooling matters more than theory at first.
Use AI as an assistant: auto-transcribe interviews, generate summary drafts, or suggest codes. Always verify everything AI produces against your raw data because AI can invent facts. That protects the tasks of defining data requirements and validating information.
Keep sensitive recordings off public AI services and follow company policy. For presentations to management or models that predict user behavior, document when AI helped and test outputs—AI should speed analysis, not replace your judgments about people.
According to the U.S. Bureau of Labor Statistics (BLS) for this occupation (SOC 15-2031.00), about 108,510 people are employed, with a median annual wage of $88,940. The lowest tenth earn about $57,060 and the top tenth about $159,910 per year.
Use those numbers as a range: entry-level or non-profit roles tend toward the low end, senior researchers or those in tech hubs with strategy responsibilities (presenting to management, building decision-support tools) can reach the top tenth.
Learn qualitative research methods (participant observation, semi-structured interviews, coding). Practice by running small field studies: recruit 5–10 participants, record sessions, and produce a short report in Google Docs that managers could use.
Also learn to analyze data and build simple predictive models or metrics—basic statistics or SPSS helps for mixed-methods projects. Put your work on GitHub or a portfolio: research protocols, consent forms, sample analyses, and final reports showing how findings changed a design.
Ethnographic UX research focuses on real people using products in context, generating rich qualitative insights and behavioral patterns. You’ll do tasks like observing systems in use, breaking them into components, and preparing management reports.
Market researchers focus more on surveys and market sizing. Data scientists focus on building predictive models and large-scale quantitative analysis. Ethnographers may use some modeling or SPSS, but the core is fieldwork, interpretation, and advising teams on design decisions.
Good note-taking and synthesis: being able to record observations clearly, tag evidence, and turn it into short, actionable findings. That ties directly to defining data requirements, validating information, and presenting results to management.
If you can quickly make a clear Google Docs report with excerpts from interviews and simple visual organization, you’ll help teams make decisions and move from observation to change.