Sr Data Analytics Engineer ATCQE

Required: Strong foundation in statistics, including general linear models and machine learning techniques (supervised, unsupervised, semi-supervised). Proficient in data visualization tools such as Tableau, Power BI, and JMP. Hands-on experience with SQL, Snowflake, and GCP BigQuery for building advanced dashboards and business intelligence reports. Solid understanding of ETL/ELT processes, data warehousing, and feature store architectures. Skilled in Python for analytics workflows, ETL/ELT pipelines, and REST API development using frameworks like Django and FastAPI. Proven ability to work in global cross-functional teams across multiple time zones. Ability to work in morning shift (6 AM IST to 3 PM IST ) Familiarity with Agile methodologies and tools such as Jira and Confluence. Exposure to MLOps practices in Google Cloud or on-premise environments. Experience with semiconductor or deep-tech manufacturing data. Understanding of manufacturing quality principles, including Advanced Process Control, Statistical Process Control (SPC), Fault Detection, and Tool Alarm systems. Knowledge of web UI frameworks, preferably Angular with Bootstrap. 5-8 years in data analytics or data science roles, preferably within the semiconductor manufacturing industry. Bachelor's or Master's degree in Computer Science, Software Engineering, Electrical/Electronics Engineering, Applied Mathematics/Statistics, or a related discipline with substantial programming experience. A Master's degree is preferred. Additional qualifications such as certifications in Machine Learning, Artificial Intelligence or Big Data, or notable achievements in Kaggle competitions, will be considered advantageous. Develop and apply statistical models and machine learning algorithms to large-scale datasets for quality and operational insights. Design predictive analytics solutions to support manufacturing and quality decision-making. Enhance existing quality control systems (e.g., SPC, Fault Detection, Tool Alarms) using advanced statistical techniques. Translate business challenges into data science solutions, including full-stack application development with user-friendly interfaces. Collaborate with Manufacturing Quality Engineers to gather requirements and deliver impactful analytics solutions. Clearly communicate model assumptions, performance metrics, and insights to stakeholders. Mentor junior engineers and share knowledge on emerging trends in statistics, machine learning, and AI. Manage multiple concurrent projects and tasks effectively through structured project management.