Accountabilities:

Mathematically formalize the trade-off between service time, RTM costs, and revenue per point of sale by developing an optimization function to replace existing heuristic approaches.

Model cost and profitability factors, including visit drivers, RTM size thresholds, and minimum margin requirements, to support customer segmentation and service-model assignment.

Develop and solve optimization models using linear programming, integer programming, and constrained optimization to determine the optimal service model for each customer.

Contribute to customer characterization across key dimensions, including Channel, Size, Potential, Territorial Profile, and Digital Autonomy, supporting expansion across multiple countries.

Analyze end-to-end P&L structures and translate financial information into decision variables, cost models, and objective functions.

Document modeling assumptions, validate results, and communicate findings with business and finance stakeholders.

Develop analyses, features, and models using Databricks in collaboration with Data Engineers and Analytics Engineers.

Promote best practices in modeling, reproducibility, and technical documentation across the team.

Requirements

Strong academic or professional background in Operations Research and Optimization, including linear programming, integer programming, and constrained optimization.

Ability to interpret P&L statements and translate financial and business considerations into variables, costs, constraints, and objective functions.

Advanced Python programming skills, including experience with optimization libraries such as PuLP, Pyomo, OR-Tools, or equivalent tools.

Solid foundations in statistics, quantitative analysis, and mathematical modeling.

Hands-on experience working with Databricks.

Strong analytical thinking and problem-solving skills, with the ability to translate business needs into robust analytical solutions.

Excellent communication skills, including the ability to explain complex technical concepts and modeling results to both technical and non-technical audiences.

Ownership, attention to detail, and the ability to collaborate effectively with cross-functional stakeholders.

Nice to have

Experience with survival models, Customer Lifetime Value (CLV), or econometrics.

Background in retail or consumer packaged goods (CPG).

Experience developing AI agents or agentic infrastructure, including Mosaic AI Agent Framework, agent orchestration, or Model Context Protocol (MCP).

Candidates based in Mexico City, particularly Santa Fe or Cuajimalpa, are preferred, although the position is not strictly on-site and applicants from other locations are welcome.

Benefits

Remote-first culture, with the flexibility to work from anywhere.

In-company English lessons.

Wellhub membership or sports club stipend.

Full coverage of certifications in AWS, dbt, Google Cloud, Azure, and Databricks.

Food credits through Pedidos Ya.

Birthday day off and an additional vacation week.

Referral bonuses for successful candidate recommendations.

Annual team trip.

Monthly childcare reimbursement.

Salary and healthcare coverage are not specified in the job description.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

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