Market Research Expert
Up to $65/hr as listed
- Listed pay
- Up to $65/hr as listed
- Field
- Writing
- Languages
- English
- Where
17 countries
United States, Germany, Brazil, India, Bangladesh, Bhutan, Indonesia, Cambodia, Sri Lanka, Malaysia, Nepal, Philippines, Pakistan, Singapore, Thailand, Timor Leste, Vietnam- Type
- Contract, remote
- Posted on SME Careers
- First seen here
- 9 October 2026
Summary
In this hourly, remote contractor role, you will work as a Market Research Subject Matter Expert (SME) to review AI-generated market research outputs and/or create expert research deliverables...
From the SME Careers listing
In this hourly, remote contractor role, you will work as a Market Research Subject Matter Expert (SME) to review AI-generated market research outputs and/or create expert research deliverables, evaluating reasoning quality and step-by-step analytical thinking while providing precise written feedback. You will assess solutions for accuracy, clarity, methodological soundness, and adherence to the prompt; identify errors in research design, sampling logic, survey construction, bias/confounding, competitive analysis, sizing (TAM/SAM/SOM), and insight synthesis; fact-check claims and data usage; write high-quality explanations and model deliverables that demonstrate correct research reasoning; and rate and compare multiple responses based on correctness, usefulness, and reasoning quality.
This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs.
Important:
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.
Responsibilities
- Develop AI Training Content: Create detailed prompts and gold-standard market research outputs (research plans, briefs, survey instruments, insight summaries, competitor scans).
- Optimize AI Performance: Evaluate and rank AI responses to improve methodological rigor, clarity, and practical usefulness.
- Ensure Model Integrity: Test AI outputs for inaccuracies, misleading claims, biased framing, and improper evidence use; validate reliability across industries and use cases.
Requirements
- Bachelor’s degree (or higher) in Marketing, Business, Economics, Statistics, Psychology, or a related field (or equivalent professional experience).
- 5+ years of professional experience in market research, consumer insights, strategy, or analytics (agency, consulting, or in-house).
- Strong understanding of qualitative and quantitative methods: interviews, focus groups, surveys, conjoint/MaxDiff familiarity (optional), basic experimental thinking.
- Proven ability to create and critique research instruments: question wording, scales, leading/loaded questions, survey flow, screening, and quality checks.
- Strong analytical reasoning: translating ambiguous questions into testable hypotheses, research plans, and decision-ready conclusions.
- Comfortable with market sizing and forecasting logic, including assumptions, triangulation, and sensitivity checks.
- Exceptional attention to detail when spotting flawed methodology, overconfident conclusions, weak evidence, or “hallucinated” data; minimum C1 English proficiency.
- Reliable, self-directed, and able to deliver consistent quality in an hourly, remote contractor workflow across time zones.
- Prior experience with AI data training/annotation, insight QA, research ops, or editorial review is strongly preferred.
Text above is the platform's own listing, shown as published. Check the details on SME Careers before applying.
Prepare for the assessment
Practice questions and what each platform says about its screening for this kind of role:
Terms in this listing
- Prompt
- The input given to a model: a question, an instruction or a conversation so far.
- Quality check
- Any review of your work by a reviewer, an automated test or a golden set. Many projects use quality scores to decide who keeps getting tasks.
- Annotation
- Adding labels or notes to data such as text, images, audio or video so a model can learn from it.
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