About the role

AI summarised

Data Scientist at GlobalFoundries, a leading semiconductor foundry. The role involves developing and deploying machine learning models to solve complex operational challenges, collaborating with cross-functional teams, and building scalable data products. Requires a Bachelor's degree in a quantitative field and 4+ years of analytics experience.

FoundryFull-timeGeneral

Key Responsibilities

Work closely with Lead Data Scientists and business customers to translate their needs into predictive and prescriptive analytics solutions.
Collaborate closely with GFIT, Manufacturing Data Domain group to support Lead Data Scientist in building the required data pipelines for productionalized and scalable predictive and prescriptive analytics solutions.
Support Lead Data Scientist to conduct exploratory data analysis and create compelling visualization using complex and high-dimensional datasets.
Help Lead Data Scientist to identify suitable machine learning or statistical approaches on type of problem and train models for optimal performance, including hyperparameter tuning.
Contribute to deploy models into production with assistance from Product Engineers.
Help Lead Data Scientist to validate and monitor models to ensure consistent performance and avoid performance degradation.
Support Lead Data Scientist during technical and analytics capability building within the analytics teams to stay current on best practices and new model techniques.
Communicate value of analytics to Fab leadership and business partners and increase the awareness of analytics Fabs.
Facilitate generating and maximizing value from analytics solutions.

Requirements

Bachelor's degree in quantitative fields such as Statistics, Mathematics, Computer Science, Operation Research, Engineering, Physics, Chemistry, Biostatistics, Economics, Data Science, or a related Engineering degree.
A minimum of 4 Years of experience in analytics space with proven track record of innovation as a forerunner.
Experience with the following types of data science techniques: Forecasting, Recommendation Systems, Logistics Optimization, Churn Analysis, Segmentation Analysis, Deep Learning
Experience in creating business models in environments such as R, Python, SAS, SPSS, or STATA (Data visualization experience is a big plus)
Experience in optimization and cloud environments to lead an inter-disciplinary team through scaling and productionisation of recommendation engines.
Experience in implementing business-focused advanced analysis that can capture industry-specific nuances.
Strong communication skills to interact with both technical and non-technical audience.
Proven leadership skills to coach and guide team members to ensure on-time excellence.
Product mindset with significant experience working in Agile teams.
Ability to build a sense of trust and rapport within the team and senior leadership.
Flexibility and ability to work with ambiguous problems and unstructured data.