Data Scientist

Entellux; México; Presencial; MXN 900.000 - 1.300.000; Jornada completa

Descripción de la vacante

Entellux group is seeking a data/ML professional to frame business challenges as analytics opportunities and lead end-to-end analytics, ML, and GenAI initiatives. You will perform explorations, build predictive and clustering models, and deploy AI solutions with proper monitoring, while collaborating with engineering teams and communicating results to executives.

You will leverage Python/SQL, GenAI tools, and GCP/AI platforms to move models from notebooks to production, ensuring rigor and

Formación

• Strong Python and SQL.
• Feature engineering, exploratory analysis, model development, and evaluation.
• Statistical reasoning and analytical problem-solving.
• Independent drive to lead analytical initiatives from discovery to deployment.
• Experience with ML pipelines and MLOps practices.
• Ability to explain technical trade-offs in plain language.

Responsabilidades

• Frame business challenges as analytics, ML, 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 controls.
• Partner with engineering teams to deploy and scale ML solutions.
• Present findings to executives in plain language.
• Review analytical approaches and raise the quality bar across the pod.

Conocimientos

Python

SQL

Statistical reasoning

Experimentation

Business problem framing

Herramientas

BigQuery

Dataflow

Looker

Looker Studio

LangGraph

LangChain

Google ADK

Descripción del empleo

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.