# 6 Data Scientist Senior – Customer Segmentation & Classification (RTM)

Muttdata

7d•Remote

About The Position

Muttdata is seeking a Senior Data Scientist to develop RTM (Route-to-Market) customer characterization models. The role will formalize an end-to-end understanding of the P&L into an optimization function that determines the optimal service model for each customer based on profitability, cost-to-serve, and potential. It focuses on turning the current heuristic trade-off between service time and revenue per point of sale into a mathematical approach that can scale across countries. The position sits at the intersection of operations research, finance, and commercial strategy. The successful candidate will collaborate with business and finance stakeholders, Data Engineers, and Analytics Engineers, developing analyses, features, and models on Databricks. Strong analytical rigor, ownership, and clear communication with technical and non-technical audiences are important.

Requirements

Background and experience in Operations Research or optimization, including linear and integer programming and constrained optimization.
Ability to interpret a P&L and translate it into variables, costs, and objective functions.
Advanced Python skills, including experience with optimization libraries such as PuLP, Pyomo, OR-Tools, or similar.
Solid foundations in statistics and modeling.
Hands-on experience with Databricks.
Ability to translate business needs into analytical solutions and communicate complex findings clearly to technical and non-technical audiences.

Nice To Haves

Experience with survival models, customer lifetime value (CLV), or econometrics.
Experience in retail or consumer goods (CPG).
Experience developing agents or agentic infrastructure, such as Mosaic AI Agent Framework, agent orchestration, or MCP.
Based in Mexico City, preferably Santa Fe or Cuajimalpa; candidates in other locations are also considered.

Responsibilities

Formalize the trade-off between service time or RTM cost and revenue per point of sale by building an optimization function.
Model cost and profitability factors, including visit drivers, RTM size thresholds, and minimum margin, to support segmentation and service-model assignment.
Build and solve optimization models, including linear or integer programming and constrained optimization, to determine the optimal service model for each customer.
Work on the characterization pillars—Channel, Size, Potential, Territorial Profile, and Digital Autonomy—and support their expansion across multiple countries.
Interpret the P&L end to end and translate it into decision variables, costs, and objective functions.
Document assumptions and validate results with business and finance stakeholders.
Develop analyses, features, and models on Databricks in collaboration with Data Engineers and Analytics Engineers.
Promote modeling, reproducibility, and documentation best practices across the team.