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Senior Data Scientist – Recommendation Systems & MLOps

Blend · Montevideo

Nuevo
Senior 🇬🇧 English
Python SQL Docker Kubernetes AWS MLOps Recommendation Systems

Descripcion del puesto

About the role

We are seeking a Senior Data Scientist with strong experience in Recommendation Systems and a solid understanding of Machine Learning and MLOps. The ideal candidate will develop and improve recommendation solutions, integrate LLM‑based capabilities when applicable, and take machine learning solutions from experimentation through production.

Key responsibilities

  • Design, develop, and optimise machine‑learning models and recommendation systems for real‑world business applications.
  • Work with large datasets using Python and SQL to explore data, engineer features, train models and evaluate results.
  • Integrate LLM‑based capabilities into existing or new machine‑learning solutions where appropriate.
  • Apply MLOps practices to support deployment, monitoring, maintenance and continuous improvement of models.
  • Collaborate with data scientists, data engineers, software engineers and other stakeholders to deliver production‑ready solutions.
  • Leverage cloud technologies and tools such as Docker and Kubernetes to enable scalable machine‑learning applications.

Required profile

  • Proven experience as a Data Scientist developing end‑to‑end machine‑learning solutions.
  • Strong knowledge of Recommendation Systems and related modelling approaches.
  • Hands‑on experience taking ML models into production and applying MLOps principles.
  • Experience with cloud‑based environments, Docker and Kubernetes.
  • Advanced English proficiency and ability to communicate technical concepts to both technical and non‑technical audiences.

Required skills

  • Python
  • SQL
  • Docker
  • Kubernetes
  • Cloud platforms (e.g., AWS)
  • MLOps
  • LLM integration
  • Recommendation Systems

What we offer

  • Learning opportunities: certifications in AWS, Databricks, Snowflake; AI learning paths; Udemy Business courses; English lessons.
  • Mentoring and development programs, career development plans.
  • Celebrations and support: special day rewards, company‑provided equipment.
  • Flexible working options to help you balance personal and professional life.

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Publicado hace 7 horas

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Montevideo