About the role
AI summarisedAs a Data Scientist at GlobalFoundries, you will play a pivotal role in the Data Science team, supporting the Lead Data Scientist to drive analytics requirements and deploy advanced predictive and prescriptive solutions. You will translate complex business needs into actionable data science projects, contributing to the scaling and productionalization of models that address operational challenges within our expanding Fab Operations.
FoundryOnsite
Key Responsibilities
- Translate business needs into predictive and prescriptive analytics solutions in collaboration with Lead Data Scientists and business customers.
- Support the building of required data pipelines for productionalized and scalable analytics solutions alongside GFIT and Manufacturing Data Domain group.
- Conduct exploratory data analysis and create compelling visualizations using complex, high-dimensional datasets under the guidance of the Lead Data Scientist.
- Assist in identifying suitable machine learning or statistical approaches and training models for optimal performance, including hyperparameter tuning.
- Contribute to deploying models into production with assistance from Product Engineers.
- Validate and monitor models post-deployment to ensure consistent performance and prevent degradation.
- Communicate the value of analytics findings to Fab leadership and business partners, increasing awareness across Fabs.
Requirements
- Bachelor’s degree in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, Physics, Chemistry, Biostatistics, Economics, Data Science, or related Engineering degree).
- A minimum of 4 years of experience in the analytics space with a proven track record of innovation.
- Experience with data science techniques such as Forecasting, Recommendation Systems, Logistics Optimization, Churn Analysis, Segmentation Analysis, or Deep Learning.
- Proficiency in creating business models using R, Python, SAS, SPSS, or STATA.
- Experience in optimization and cloud environments related to scaling and productionalizing recommendation engines.
- Strong communication skills capable of interacting with both technical and non-technical audiences.
- Product mindset with significant experience working in Agile teams.