This is a fully remote position, open to applicants in Mexico.

📋 Description

• Mathematically define the trade-off between service time, RTM cost, and revenue per point of sale, creating the optimization function that is currently functioning heuristically.

• Model the cost and profitability factors (visit drivers, RTM size thresholds, minimum margin) that contribute to the segmentation and assignment of service models.

• Construct and resolve optimization models (linear/integer programming, constrained optimization) to identify the optimal service model for each customer.

• Collaborate on the characterization pillars (Channel, Size, Potential, Territorial Profile, Digital Autonomy) and their expansion across multiple countries.

• Analyze the P&L from start to finish and convert it into decision variables, costs, and objective functions.

• Document assumptions and validate findings with business and finance stakeholders.

• Create analyses, features, and models on Databricks, working alongside Data Engineers and Analytics Engineers.

• Encourage best practices in modeling, reproducibility, and documentation throughout the team.

⛳️ Requirements

• Proven background and experience in Operations Research / Optimization (linear and integer programming, constrained optimization).

• Capability to interpret a P&L and translate it into variables, costs, and objective functions.

• Proficient in advanced Python, including optimization libraries (PuLP, Pyomo, OR-Tools, or similar).

• Strong foundation in statistics and modeling.

• Practical experience with Databricks.

• Ability to convert business needs into analytical solutions and effectively communicate complex findings to both technical and non-technical audiences.