Frame business challenges as analytics, machine learning, or AI opportunities.
Perform exploratory data analysis and identify actionable business insights.
Build predictive, classification, clustering, forecasting, optimization, and recommendation solutions.
Develop GenAI applications leveraging LLMs, RAG architectures, and agentic workflows.
Run AI applications in production with monitoring, evaluation, and operational controls.
Partner with engineering teams to deploy and scale machine learning solutions.
Present findings to executives and explain technical trade-offs in plain language.
Review analytical approaches, mentor Grade 8 team members, and raise the quality bar across the pod.
Strong Python and SQL.
Feature engineering, exploratory analysis, model development, and evaluation.
Statistical reasoning and analytical problem-solving.
You can independently drive analytical initiatives from discovery to deployment.
Statistics and experimentation Hypothesis testing.
A/B testing.
Regression analysis.
Probability theory.
Statistical significance and confidence intervals.
You make decisions using evidence rather than intuition.
Machine learning Classification and regression.
Clustering and segmentation.
Forecasting.




Recommendation systems.
You understand both algorithm selection and business applicability.
Google Cloud Platform and AI Platforms (Preferred) Strong hands-on experience with:
BigQuery
Dataflow
Looker
Looker Studio
You understand how to move solutions from notebooks into production environments.
LLM engineering and GenAI Retrieval-Augmented Generation (RAG).
Tool-calling agents.
Context management.
Knowledge-grounded AI.
Agent orchestration.
Working knowledge of frameworks such as: LangGraph
LangChain
Google ADK
You understand when to use traditional ML, GenAI, or a hybrid approach.
MLOps and AI evaluation Experiment tracking.
Automated evaluations.
Prompt and model versioning.
Regression testing.
You can prove a solution improved outcomes, not merely claim it.
AI-assisted analytics Copilot, Gemini, Claude, Cursor, or similar tools as part of your normal workflow.
Thoughtful use of AI to accelerate analysis without compromising rigor.
Communication Executive storytelling.
Business problem framing.
You can explain a model to a non-technical executive without losing accuracy.

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📌 Data Scientist (Ciudad de México)
🏢 Entellux
📍 Ciudad de México