E-commerce: A 5-Person Data & AI Pod Built Before Peak Sales Season

S.N's category managers were pulling sales numbers from manual CSV exports, a full day behind on every decision, and leadership wanted a real recommendation engine live before their biggest annual sales event, ten weeks out. That meant hiring five roles at once: a Data Engineer to build the warehouse, a Data Scientist and AI Engineer to build the recommendation model, an Analytics Engineer to keep category managers self-service, and a QA Engineer to keep it all reliable under peak load. Two local recruiter shortlists and S.N's own postings, run in parallel for all five roles, produced only candidates with basic reporting-SQL experience - nobody with production ML or pipeline experience, and no time to keep searching.

S.N needed five different hires against a fixed seasonal deadline, with no time for five sequential searches. GTEMAS matched candidates for all five roles in parallel from the Talent Ecosystem, each with a verified Skill Profile scoring the exact skill the role needed, including production dbt/Snowflake and recommendation-model experience. S.N interviewed 2-3 pre-matched candidates per role and made every final call themselves. Onboarding: GTEMAS supported offer negotiation and the transition for each hire, then stepped back - all five joined S.N's own team and payroll directly.

The recommendation engine went live two weeks before S.N's peak sales event, cutting reporting lag from a full day to same-day along the way. S.N's Head of Analytics noted that because every candidate's pipeline and ML experience was already verified before the interview, five parallel searches still felt manageable - the conversations were about how each hire would work with the team, not whether they knew the tools.