Senior Data Scientist – Customer Segmentation & Classification @ Muttdata — Vaga Remota

What We Do

Leveraging our expertise, we build modern Machine Learning systems for demand planning and budget forecasting.

Developing scalable data infrastructures, we enhance high-level decision-making, tailored to each client.
Offering comprehensive Data Engineering and custom AI solutions, we optimize cloud-based systems.
Using Generative AI, we help e-commerce platforms and retailers create higher-quality ads, faster.
Building deep learning models, we enhance visual recognition and automation for various industries, improving product categorization, quality control, and information retrieval.
Developing recommendation models, we personalize user experiences in e-commerce, streaming, and digital platforms, driving engagement and conversions.

Formalize mathematically the trade-off between service time / RTM cost and revenue per point of sale, building the optimization function that currently operates heuristically.
Model the cost and profitability layer (visit drivers, RTM Size thresholds, minimum margin) that feeds the segmentation and the assignment of service models.
Build and solve optimization models (linear/integer programming, constrained optimization) to determine the optimal service model for each customer.
Work on the Characterization pillars (Channel, Size, Potential, Territorial Profile, Digital Autonomy) and their multi-country expansion.
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, collaborating with Data Engineers and Analytics Engineers.
Promote best practices in modeling, reproducibility, and documentation across the team.

Background and experience in Operations Research / Optimization (linear and integer programming, constrained optimization).
Financial context: ability to interpret a P&L and translate it into variables, costs, and objective functions.
Advanced Python, including optimization libraries (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.

Experience with survival models / CLV and econometrics.
Experience in retail or consumer goods (CPG).
Experience developing agents / agentic infrastructure (e.g., Mosaic AI Agent Framework, agent orchestration, MCP).
Preferably Mexico City (Santa Fe / Cuajimalpa). Although the position is not strictly on-site, we give preference to candidates based in Mexico City; however, we are open to other locations.