Ingeniero Senior, Ciencia de Datos

About the role

Job Description Ciudad de México Nivel de estudios Bachelor's Degree in Computer Science, Computer Engineering, Data Science, Artificial Intelligence, Industrial Engineering or equivalent. Advanced degree preferred. Experiencia Requerida 5+ years delivering enterprise-level Data Science solutions Habilidades Soft Skills Team Player: Excellent communication and interpersonal skills, working effectively as part of a team and collaborating with stakeholders across different locations. Ability to tell a story with data. Ability to communicate results of advanced analytics in plain English. Adaptability: Thrive in a dynamic, fast-paced work environment and adapt quickly to changing business needs. Attention to Detail: Meticulous attention to detail, ensuring data accuracy and maintaining high-quality standards in all deliverables. Technical Skills A minimum of 5 years of professional hands-on experience developing/deploying Enterprise-scale data scicene soluitons An understanding of cloud infrastructure for Machine Learning/AI ops., e.g., Databricks / AWS Sagemaker Strong problem-solving skills and the ability to troubleshoot complex data and machine learning related issues Solid understanding of a variety of machine learning algorithms. Solid understanding of developing, deploying and gaining value from Gen AI and AI (agentic) agents. Expert-level proficiency with Python Hands-on experience with machine learning and generative AI frameworks Experience deploying machine learning and AI applications in cloud-based solutions (AWS or Databricks). Strong understanding of database systems, data structures, and data processing techniques. Familiarity with OPC-UA, MQTT, and industrial automation protocols is a significant advantage Strong analytical abilities to translate business requirements into technical AI workflows Experience in a manufacturing or industrial environment preferred Resumen The Sr. Data Scientist, Digital Manufacturing uses data from production lines (including sensors), digital frontline worker solutions, and quality control systems to build and deliver advanced analytics, predictive modeling, machine learning, and AI solutions that improve manufacturing performance and reduce cost. The role applies statistical methods, data visualization, generative AI, and AI agents to help teams make data-driven decisions that increase productivity and efficiency, reduce waste, improve product quality, and lower total operating costs. Key focus areas include manufacturing process optimization, predictive maintenance, defect detection, utilities (energy/water) optimization, and process automation. You will be responsible for the entire project life cycle from conception through commissioning of projects that support advanced analytic initiatives. Responsabilidades 1. Develop and operationally deploy advanced analytic decision support solutions (machine learning, deep learning, generative AI, Agentic agents, etc.) to that incorporate data from disparate manufacturing systems (MES, LIMS, CMMS, WMS, ERP, etc.) to drive process improvement and performance optimization. 2. Collaborate with cross-functional teams (Manufacturing, Operations, R&D, Quality, IT, etc.) to design and implement largescale data-driven solutions. Ensure data quality, integrity, and compliance with model risk standards. Partner with technical and non-technical teams to resolve data gaps, inconsistencies, support validation activities and ensure ROI is derived from data science activities. 3. Collect, clean, and pre-process large structured and unstructured datasets to ensure they are "model-ready". Preprocess, cleanse, and manage data to ensure accuracy and quality for model input and inference. 4. Conduct Exploratory Data Analysis (EDA) using statistical techniques and data visualization to uncover patterns, relationships, and outliers. Conduct feature engineering and train/test machine learning models for real-time process monitoring/alerting, anomaly detection, quality defects and machine failure prevention/identification. Manage model lifecycle (data prep, train, test, tune, deploy, monitor performance). 5. Design, build, and deploy AI-powered applications, chatbots, and agents. Implement Retrieval-Augmented Generation (RAG). Integrate pre-trained AI models with internal data sources and external applications. 6. Optimize AI models for speed, scalability, and cost-efficiency in production environments. 7. Monitor deployed models for performance, accuracy, and latency, implementing ML/LLM Ops and using evaluation frameworks. 8. Work with technical and non-technical teams to implement models into production and translate complex findings into actionable insights for stakeholders. 9. Maintain documentation, best practices, and knowledge repositories for analytics solutions. Monitor adoption KPIs, gather user feedback, and drive continuous improvement in analytics capabilities. 10. Stay updated with the latest AI trends to enhance organizat

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Company Intel

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