Sobre esta vaga de # 7 - Senior AI Data Scientist / Modeler - Databricks - Latam na Muttdata
Muttdata · Remoto · Peru
🚀 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.
This opportunity is with a leading multinational beverage company based in Mexico City, as part of its AI Factory initiative.
We are looking for a Senior AI Data Scientist / Modeler with Databricks experience to join our team 🐶🚀. You'll build the Sales Leader Insights product within the Smart Force platform: an agentic AI architecture that selects the most relevant insights for each sales leader and recommends a coaching route that makes business sense, balancing geographic proximity with route priority and criticality so field coaching is both optimal and executable.
This role sits at the intersection of agentic AI, recommendation systems, and operations research, working closely with data, platform, and business teams across the end-to-end AI Factory cycle — from governed features and data to model development, orchestration, and productionization. Strong technical depth, business sense, and ownership are essential to succeed in this fast-paced, collaborative environment.
🚀 What We Do
🌟 Our Partnerships
🌟 Our Values
Responsibilities 🤓
- 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 🚀
Nice to Have 😉
🎁 Perks
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