Staff Data Scientist — Production ML & Generative AI

QuintoAndar

EE. UU.

Híbrido

USD 140.000 - 200.000

Jornada completa

14 días+

Una candidatura completa en un minuto

Currículum y carta de presentación personalizados, listos para enviar.

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Ventajas ofrecidas por este puesto de trabajo

Remote-first
Profit sharing
Health insurance
Childcare subsidy
Home office allowance
Employee assistance program

Descripción de la vacante

QuintoAndar is seeking a staff-level data scientist embedded in product squads to ship ML-driven features across search, recommendations, pricing, credit, and conversational systems. The role focuses on productionizing models and advancing generative AI capabilities.

You'll blend hands-on modeling with technical leadership, mentor peers, and contribute to the data science community of practice through reviews, demos, and guild activities.

Formación

  • 3+ years applying data science and deploying ML models in production.
  • Strong foundations in regression, classification, clustering, neural nets, feature selection, cross-validation, explainability.
  • Understanding the engineering challenges of deploying ML systems.
  • Proficiency in Python with some deep learning experience.
  • Experience providing technical leadership or mentoring to data scientists.

Responsabilidades

  • Architect, deploy ML systems to production focusing on chatbots and search.
  • Design experiments and analyze data to improve models.
  • Develop LLM-powered applications including prompt testing and guardrails.

Conocimientos

ML fundamentals
Python
Production ML
Technical leadership
English communication
Mentoring

Educación

Master's degree preferred

Descripción del empleo

QuintoAndar is seeking a staff-level data scientist embedded in product squads to ship ML-driven features across search, recommendations, pricing, credit, and conversational systems. The role focuses on productionizing models and advancing generative AI capabilities.

You'll blend hands-on modeling with technical leadership, mentor peers, and contribute to the data science community of practice through reviews, demos, and guild activities.

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