senior data scientist – key requirements

- 5–8 years of hands-on experience in data science, with a strong track record of delivering production-grade solutions in business environments.
- deep expertise in traditional machine learning techniques, including time series forecasting (e.g., arima, sarima), regression models, and other statistical methods applied to real-world datasets.
- proven experience designing and implementing optimization models (e.g., linear programming, mixed-integer programming, heuristics) to solve complex business problems such as demand planning, inventory optimization, or resource allocation.
- recent, demonstrable experience working on generative ai initiatives, including llm-based solutions and agentic workflows (e.g., autonomous agents, multi-step reasoning pipelines, tool-augmented ai systems).
- hands-on experience in designing, deploying, and operationalizing agentic workflows in production environments, including orchestration, tool integration, monitoring,



and performance optimization of ai-driven agents.
- strong domain knowledge in consumer-packaged goods (cpg), with hands-on experience in at least one functional area: sales (e.g., demand forecasting, pricing), finance (e.g., revenue/margin analytics), or supply chain (e.g., planning, logistics, inventory).
- ability to translate business problems into scalable analytical and ai-driven solutions, partnering closely with cross-functional stakeholders such as product managers, engineers, and business teams.
- 100% hands-on role with active involvement in data exploration, model development, evaluation, deployment, and continuous improvement.
- proficiency in python and relevant data science ecosystems (e.g., pandas, scikit-learn, statsmodels, pytorch/tensorflow), along with experience in cloud platforms and mlops practices.

📌 Data scientist (México)
🏢 Entellux
📍 México