About Velou

Velou is a Silicon Valley AI technology startup building the product-data infrastructure behind the next generation of ecommerce. We turn raw catalogs into rich attributes and product copy and power smart discoverability, search, recommendations, and AI shopping agents customers buy through.
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The Role

As an AI Data Analyst, you'll take ownership of a genuine slice of the enrichment engine that powers Velou's products. You'll work directly with our in-house LLM models and shape the taxonomies and product intelligence.

● Location: Remote

● Eligibility: Current students (second year and above) and recent graduates.
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What you'll do

● Support our AI-driven enrichment workflows and help build the taxonomy and product-intelligence structures behind them - the categories, attributes, and relationships that make a catalog discoverable

● Bring cultural and brand context to product data enrichment - reading the intent behind how customers shop, what a brand stands for, and who buys it, then translating that into how products are surfaced

● Work hands-on with our in-house LLM models and AI tooling by helping fine-tune them and shaping how they behave

● Test and evaluate model outputs: assess enriched data for accuracy and comprehensiveness, and working with cross-functional teams to improve quality

● Turn client needs and business goals into concrete enrichment requirements

● Document edge cases and contribute to clearer guidelines and standards
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What we're looking for

● Strong cultural fluency is one of the most important skills we're hiring for - built through any combination of study, work, media, or lived experience.

● Strong fluency in English, written and spoken. Clarity and precision in English are essential to the role.

● A student from a creative or commercial field - fashion, design, marketing, media, or communications. Strong applicants from other backgrounds are welcome - we care more about how you think than what you studied.

● An analytical mind that prefers judgment over box-ticking - you see patterns and reason clearly about how things should be structured, and can justify a call.

● Rigorous about data quality, structure, and reproducibility; you test your assumptions rather than trust them.

● Active interest in consumer culture and retail trends, in a cultural and global context, more specifically US consumer culture - have a working awareness of how consumer behavior differs across markets.

● Hands-on experience with AI-assisted workflows or tools, with some exposure to prompt engineering

● Comfortable in a fast-paced, evolving environment.

● Proficiency with data analysis tools and spreadsheets and basic LLM literacy is a plus.
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What you'll gain

● A funded Prompt Engineering course certification as part of your onboarding, alongside in-house training on LLM workflows, fine-tuning, and AI-assisted tooling.

● A collaborative, forward-thinking environment alongside experienced data analysts and engineers.

How to apply

Send your CV plus one work sample. We use the sample to see how you think and how you communicate in English - both core to this role. Pick one:

Option A - A written piece (500–900 words). A review, analysis, or reflection on a situation, project, or event of your choosing. A brand or campaign you had a take on, a project you led, an experience that shifted how you think - anything where your reasoning is on display. We're more interested in how you think about it than the topic itself.

Option B - An artifact you're proud of. Research, a brand or campaign breakdown, writing, a design or creative project, a prototype, a coursework piece - whatever genuinely reflects what you do well.

With either option, include a framing summary: what the piece is, what you set out to do, and what you'd like us to notice about it. For Option B, if your artifact isn't immediately self-explanatory, please walk us through what we're looking at and the order to view it in.

Originality matters. The work needs to be your own i.e. your thinking, your writing, not a collaboration and not AI-generated. Light use of AI tools (for proof-reading) is fine if you mention it. Cite sources where relevant.
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How to share your work sample:

● Your CV as a separate PDF

● Written submission (Option A): a second PDF with your write-up + framing summary

● Artifact (Option B): a second PDF containing your framing summary and a link to the work (portfolio, public repo, Figma), or a ZIP file with your work + framing summary inside

● For large files (over 25MB) - use a download link (Google Drive) rather than emailing the file directly.

Please name your submissions: Firstname_Lastname_AIDA_CV & Firstname_Lastname_AIDA_Application. Send both to careers@velou.com