Data Scientist

You are seeking a Data Scientist at noon Food to build and scale data-driven and AI-powered systems that directly impact product and business outcomes. This role is hands-on, with a focus on applied machine learning and Large Language Model (LLM) systems. - Frame ambiguous product or business problems into clear data science or ML objectives. - Build, train, and evaluate machine learning models including classification, regression, clustering, forecasting, and recommendations. - Design and implement LLM-based systems such as Retrieval-Augmented Generation (RAG) pipelines, vector embeddings, semantic search, and ranking. - Combine classical ML with LLMs to balance accuracy, cost, latency, and interpretability. - Partner with engineering to deploy models and LLM systems into production, monitor performance, and drift. - Communicate trade-offs, results, and recommendations clearly to technical and non-technical stakeholders. Qualifications Required: - 2+ years of experience in Data Science or Machine Learning roles. - Strong hands-on experience in Python (pandas, numpy, scikit-learn) for production-grade notebooks and scripts. - Solid understanding of model selection, evaluation, and feature engineering. - Proven experience building RAG pipelines using vector databases. - Hands-on experience with frameworks such as LangChain, LlamaIndex, or similar for LLM Systems. - Strong SQL skills for working with large analytical datasets. - Understanding of experimentation, validation, and real-world model limitations. Nice to Have: - Experience deploying ML or LLM systems in production environments. - Familiarity with MLOps, model monitoring, or feature stores. - Experience with unstructured data like text, documents, feedback, or logs. - Background in recommendation systems, search, ranking, or optimization. This role is not focused on reporting or dashboards, nor is it a research-only or academic ML role. It is centered on shipping practical ML and LLM-powered systems. You are seeking a Data Scientist at noon Food to build and scale data-driven and AI-powered systems that directly impact product and business outcomes. This role is hands-on, with a focus on applied machine learning and Large Language Model (LLM) systems. - Frame ambiguous product or business problems into clear data science or ML objectives. - Build, train, and evaluate machine learning models including classification, regression, clustering, forecasting, and recommendations. - Design and implement LLM-based systems such as Retrieval-Augmented Generation (RAG) pipelines, vector embeddings, semantic search, and ranking. - Combine classical ML with LLMs to balance accuracy, cost, latency, and interpretability. - Partner with engineering to deploy models and LLM systems into production, monitor performance, and drift. - Communicate trade-offs, results, and recommendations clearly to technical and non-technical stakeholders. Qualifications Required: - 2+ years of experience in Data Science or Machine Learning roles. - Strong hands-on experience in Python (pandas, numpy, scikit-learn) for production-grade notebooks and scripts. - Solid understanding of model selection, evaluation, and feature engineering. - Proven experience building RAG pipelines using vector databases. - Hands-on experience with frameworks such as LangChain, LlamaIndex, or similar for LLM Systems. - Strong SQL skills for working with large analytical datasets. - Understanding of experimentation, validation, and real-world model limitations. Nice to Have: - Experience deploying ML or LLM systems in production environments. - Familiarity with MLOps, model monitoring, or feature stores. - Experience with unstructured data like text, documents, feedback, or logs. - Background in recommendation systems, search, ranking, or optimization. This role is not focused on reporting or dashboards, nor is it a research-only or academic ML role. It is centered on shipping practical ML and LLM-powered systems.