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

Remote

San Francisco (CA)

Remote

USD 120,000 - 180,000

Full time

3 days ago

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Benefits offered by this job

Remote-friendly

English lessons

Wellhub stipend

Certifications covered

Food credits

Birthday off

Referral bonuses

Mutters' Trip

Childcare reimbursement

Job summary

Muttdata is a remote-first startup building data products and ML solutions. We seek a Senior Data Scientist focused on customer segmentation, RTM trade-offs, and multi-country optimization.

You will formalize heuristics into scalable models and collaborate with business, finance, data engineers, and analytics engineers. Responsibilities include designing optimization, interpreting P&L impacts, and delivering reproducible analyses with Databricks.

Qualifications

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.

Responsibilities

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.

Skills

Operations Research / Optimization

P&L interpretation

Advanced Python

Statistics & modeling

Databricks

Clear communication

Tools

PuLP

Pyomo

OR-Tools

Databricks

Job description

Join Our Remote Data Products & Machine Learning Startup!

At Muttdata, we build innovative Data Products and Machine Learning solutions that help companies solve complex business challenges. As a fast-growing, remote-first startup, we’re passionate about technology, collaboration, and continuous learning.

We are looking for an Data Scientist Senior – Customer Segmentation & Classification (RTM) to join our team.

This role sits at the intersection of operations research, finance, and commercial strategy. Today, the trade-off between service time and revenue per point of sale is handled heuristically. Your mission is to formalize it mathematically and make it scalable across countries. You will work closely with business and finance stakeholders, as well as Data Engineers and Analytics Engineers. Strong analytical rigor, ownership, and the ability to communicate with non-technical audiences are essential.

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.

Our Partnerships

Amazon Web Services
Astronomer
Databricks

Our Values

We are Data Nerds
We are Open Team Players
We Take Ownership
We Have a Positive Mindset

Curious about what we’re up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects we’re working on!

Responsibilities

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.

Required Skills

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.

Nice to Have

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.

Perks