object storage , Kubernetes), Containers and orchestration (Docker, Python, Distributed query and compute engines, Model registries, Efficient columnar and array data formats, Large-scale data systems, CI/CD for ML

Pyspark, Apache Spark, Kafka, Sql, Git, Databricks, Python, RAG solution stacks, embeddings, CI CD workflows, vector databases, cloud-native observability, Delta Lake

Python, NAND technology, ML-based data analytics, Origin, semiconductor process integration, Data Analysis, CMOS fundamentals, Statistical Techniques, Ai, memory device operation, yield reliability variability, semiconductor device physics, Jmp, failure analysis methodologies, process-device interactions, Experimental Design, Mini-Tab, memory development

Prototyping, Pyspark, Data Modeling, Automation, Sql, Python, Data ingestion, ETL pipelines, AI-enabled data solutions

data wrangling , Ml, Data Analytics, Optimization Algorithms, Python, cleaning and validation, Ai, ML libraries, modern AI

Google Looker, Sql, Python

Python software programming, Evaluation and testing techniques

Performance Tuning, Pyspark, Data Modeling, Tableau, API design, Sql, Git, Databricks, Python, Airflow, Data warehouse design, dbt, Data orchestration, ML GenAI or LLM applications, CI/CD practices

causal inference , simulation modeling , Power Bi, Tableau, Sql, Python, Prompt engineering, AI LLM-assisted workflows, Looker, R, Copilots, AI agents, ETL pipelines