Data Scientist Engineer - IT - Mercado Envios

Mercado Libre•Tulsa, OK

17d•Hybrid

About The Position

As a Data Scientist at Mercado Libre, you will design and scale innovative and secure systems that solve real-world, high-impact problems. You will work in a dynamic environment with cutting-edge technology, applying good engineering practices, proprietary AI models, and continuous learning, with the purpose of democratizing e-commerce and financial services in Latin America. Imagine undertaking challenging, dynamic, and innovative projects and being responsible for: Implementing best coding practices for model development and deployment, including object-oriented programming. Presenting and explaining your results, adapting your message effectively to different levels within the company (Analysts, Leaders, or Manager/Expert). Translating business/operational problems into technical terms and proposing ad-hoc solutions based mainly on statistics/machine learning for time series, being able to design scalable solutions, estimate the development effort of tasks, and define their priorities, being proactive in decision-making during development. Monitoring, maintenance, and evolution of forecasting models for the consumption of shipping teams, identifying opportunities for improvement. Cooperating with the development cycle of the entire team, being a technical reference and supporting the team's sizing and technical unblocking. Summarizing ideas, learnings, and best practices to transfer knowledge to peers or other MELI areas.

Requirements

Proficiency in Python, SQL, and GIT.
Knowledge of object-oriented programming.
Proficiency in statistics and data science.
Specific knowledge in time series and forecasting models.
Experience with feature engineering.
Applied Machine Learning knowledge.
Understanding of the end-to-end ML model development flow to production.

Responsibilities

Implement best coding practices for model development and deployment, including object-oriented programming.
Present and explain results, adapting the message to different company levels.
Translate business/operational problems into technical terms and propose statistical/machine learning solutions for time series.
Design scalable solutions, estimate development effort, and define task priorities.
Be proactive in decision-making during development.
Monitor, maintain, and evolve forecasting models for shipping teams.
Identify opportunities for model improvement.
Cooperate with the team's development cycle.
Act as a technical reference and support team sizing and technical unblocking.
Summarize ideas, learnings, and best practices to transfer knowledge.