Sebastián Quintero Rojas
About Sebastián Quintero Rojas
Sebastián Quintero Rojas is a Lead Decision Scientist with extensive experience in optimization and simulation tools for automated decision-making. He has held various roles in notable companies such as Rappi and Mercado Libre, and he currently works at Nextmv, where he developed the router engine.
Current Role at Nextmv
Sebastián Quintero Rojas serves as the Lead Decision Scientist at Nextmv. He has held this position since 2021, contributing to the development of advanced decision-making tools. His work focuses on enhancing automated decision-making processes through optimization and simulation techniques. Notably, he developed the router engine at Nextmv, which is documented for user reference.
Previous Experience at Nextmv
Before his current role, Sebastián worked as a Senior Decision Scientist at Nextmv for four months in 2021. During this time, he contributed to projects that aimed to improve decision-making frameworks within the organization. His transition to Lead Decision Scientist reflects his growing responsibilities and expertise in the field.
Experience at Rappi
Sebastián held multiple positions at Rappi, where he focused on dispatch and routing operations. He served as an Operations Research Scientist from 2018 to 2019, then as Operations Research Lead for nine months in 2020, and finally as a Senior Operations Research Engineer for one year until 2020. His roles involved building, testing, and deploying mathematical optimization models to enhance operational efficiency.
Education and Expertise
Sebastián Quintero Rojas studied at Universidad de Los Andes, where he earned a Bachelor of Science in Mechanical Engineering and a Bachelor of Science in Industrial Engineering. He also completed a Master of Science in Industrial Engineering with a focus on Operations Research. His academic background supports his strong emphasis on optimization and simulation tools in decision-making.
Technical Skills and Contributions
Sebastián possesses expertise in a diverse technology stack, including Go, Python, R, Docker, Git, SQL, and NoSQL. He has presented a tutorial on operationalizing Python-based Pyomo MIP decision models and has featured pre-bundled solvers for CBC and GLPK. His technical skills are integral to his role in developing and implementing optimization solutions.