Data Science
π 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.
π Our Partnerships
- Amazon Web Services
- Astronomer
- Databricks
π Our Values
- π We are Data Nerds
- π€ We are Open Team Players
- π We Take Ownership
- π We Have a Positive Mindset π Curious about what weβre up to? Check out our case studies and dive into our blog post to learn more about our culture and the exciting projects weβre working on! π
Responsibilities π€
- Design and build the agentic AI architecture that generates and prioritizes actionable insights for sales leaders, deciding which ones truly matter based on management focus, coaching needs, and performance.
- Model the coaching route recommendation as a routing optimization problem, balancing geographic proximity and route priority/criticality while avoiding inefficient trips (e.g., coaching a pre-seller for an hour and then sending them to another city).
- Integrate business signals (potential, execution, compliance, pre-seller KPIs) so coaching recommendations make sense both operationally and geographically.
- Enable agentic decision-making on coaching optimality: the AI agent evaluates and adjusts recommendations based on time, distance, and criticality constraints.
- Package the model under the MFL framework (PyFunc, model_card, tests) and integrate it with orchestration and deployment to operations.
Required Skills π
- Proven experience as a Data Scientist / Modeler in supervised machine learning and recommendation or prioritization systems.
- Hands-on experience in agentic AI development (designing AI agents that make decisions).
- Experience in optimization / operations research: routing, VRP, constrained optimization.
- Advanced Python (scikit-learn; optimization libraries such as OR-Tools, Pyomo, PuLP, or similar).
- Experience with MLflow and model development in Databricks.
- Experience working with geospatial / routing data (distances, geolocation).
Nice to Have π
- Previous experience with sales forces, commercial coaching, or field/route optimization solutions.
- Experience developing AI agents / agentic infrastructure (e.g., Mosaic AI Agent Framework, agent orchestration, MCP).
- Familiarity with Unity Catalog, Databricks Asset Bundles, and Azure DevOps.
- Location: Mexico City (Santa Fe / Cuajimalpa) preferred, though the role is not strictly on-site and weβre open to other locations.
π Perks
- Remote-first culture β work from anywhere! π
- AWS, DBT, Google Cloud, Azure & Databricks certifications fully covered
- Birthday off + an extra vacation week (Mutt Week! ποΈ)
- Referral bonuses β help us grow the team & get rewarded!
- Maslow: Monthly credits to spend in our benefits marketplace.
- βοΈποΈ Annual Mutters' Trip β an unforgettable getaway with the team!
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