What you'll do

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.

Evaluate models using rigorous statistical frameworks.

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.

Expertise required

Data science

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.

Experimental design.

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.

Ensemble methods.

Model explainability.





You understand both algorithm selection and business applicability.

Google Cloud Platform and AI Platforms (Preferred)

Strong hands-on experience with:

- Vertex AI
- Gemini Models
- BigQuery
- Vertex AI Search
- Vertex AI Agent Builder
- Cloud Storage
- Dataflow
- Looker
- Looker Studio

You understand how to move solutions from notebooks into production environments.

LLM engineering and GenAI

Prompt engineering.

Retrieval-Augmented Generation (RAG).

Tool-calling agents.

Context management.

Knowledge-grounded AI.

Agent orchestration.

Working knowledge of frameworks such as:

- LangGraph
- LangChain
- Google ADK
- Vertex AI Agent Builder

You understand when to use traditional ML, GenAI, or a hybrid approach.

MLOps and AI evaluation

Model monitoring.

Drift detection.

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.

Strong validation discipline.

Thoughtful use of AI to accelerate analysis without compromising rigor.

Communication

Executive storytelling.

Data visualization.

Client presentations.

Business problem framing.

You can explain a model to a non-technical executive without losing accuracy.

📌 Data Scientist (México)
🏢 Entellux
📍 México