About GlobalFoundries
GlobalFoundries is a leading full-service semiconductor foundry providing a unique combination of design, development, and fabrication services to some of the world’s most inspired technology companies. With a global manufacturing footprint spanning three continents, GlobalFoundries makes possible the technologies and systems that transform industries and give customers the power to shape their markets. For more information, visit www.gf.com.
Introduction:
As a Principal Staff Data Scientist, you will be reporting to Analytics Center of Excellence (CoE) Manager and support in a pivotal role within the Data Science team and assist in the provision analytics technical requirements. You will initiate the best-practices in analytics use case development/deployment/ scaling/maintaining the data science projects and fostering partnership with Artificial Intelligence (AI) vendors/start-ups. You are responsible for developing and deploying machine learning or other statistical models to address most complex and operational challenges. You will build scalable data products that will drive value and collaborate with all stakeholders across GlobalFoundries.
Your Job:
- Work closely with stakeholders to translate their needs into predictive and prescriptive analytics solutions.
- Collaborate closely with GFIT and Manufacturing Data Domain group to build required data pipelines for productionalized and scalable predictive and prescriptive analytics solutions.
- Conduct exploratory data analysis and create compelling visualization using complex and high-dimensional datasets.
- Identify suitable machine learning or statistical approaches on type of problem and train models for optimal performance, including hyperparameter tuning.
- Deploy models into production with assistance from Product Engineers.
- Validate and monitor models to ensure consistent performance and avoid performance degradation.
- Lead technical and analytics capability building within the analytics teams to stay current on best practices and new model techniques.
- Lead team of data scientists, including managing workloads, creating, and implementing common modeling standards and practices, and providing mentorship, support, and expertise.
- Communicate value of analytics to Fab leadership and business partners and increase the awareness of analytics Fabs.
- Responsible for generating and maximizing value from analytics solutions.
Other Responsibilities:
- Perform all activities in a safe and responsible manner and support all Environmental, Health, Safety & Security requirements, and programs.
Required Qualifications:
- Bachelor’s degree in a quantitative field such as Statistics, Mathematics, Computer Science, Operation Research, Engineering, Physics, Chemistry, Biostatistics, Economics, Data Science, or a related Engineering degree.
- A minimum of 10 Years of experience in analytics space with proven track record of innovation as a forerunner.
- 5+ years of experience in technical team leadership.
- 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 audiences.
- 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.
- Strong bias towards action and proven ability toward iteratively and quickly to show incremental impact and value.
Preferred Qualifications:
- Master’s in data science, computer science or other quantitative field.
