Data Scientist
3 tygodni temu
We are seeking a highly skilled Senior Machine Learning Engineer to join our team at NielsenIQ.
This is a unique opportunity to showcase your expertise in designing, developing, and deploying machine learning solutions at scale in cloud-based environments.
As a Senior Machine Learning Engineer, you will be responsible for optimizing and standardizing data science and machine learning solutions, working closely with engineering teams to bridge the gap between data science and software engineering.
You will also be responsible for developing end-to-end analytical approaches based on product concepts, working with NIQ's Point of Sale/third-party data to develop data science solutions for implementation and standardization in production environments.
Key Responsibilities- Design, develop, test, deploy, maintain, and enhance ML solutions using software engineering best practices.
- Study and convert data science prototypes to production-quality ML solutions.
- Implement machine learning solutions at a large scale in on-premises or cloud-based environments.
- Implement end-to-end management of the model lifecycle using MLOps practices.
- Create and improve methods and tools, and contribute to best practices in the area of data science.
- Foster ML-related knowledge exchange within NIQ, especially around code quality and engineering best practices.
- Contribute to Communities of Practice.
- Present NIQ's MLE/MLOps expertise at conferences and workshops.
- Support the development of talent and organize required trainings.
- Master or Bachelor degree that reflects computer science/engineering and statistical/mathematical skills.
- Experience in machine learning software development and architectures for machine learning.
- Solid experience with production-level code quality metrics and working closely with software and testing engineers.
- Expert skills in Python and the respective machine learning ecosystem.
- Expert skills in working in (big) database environments (RDBMS, Postgres, BigQuery).
- Solid statistical skills: either in survey statistics (sampling, non-response adjustments, Bayesian statistics), or econometric modeling (program evaluation, forecasting, economic theory, panel data), or observational causal inference (potential outcomes, structural equation models, instrumental variables), or experimental design.
- Typically 5+ years of work experience.
- Solid experience with agile development practices.
- Good to have experience in machine learning frameworks such as MLflow.
- Good to have experience or knowledge of Kubeflow, Airflow, Docker, Kubernetes.
- Good to have experience in cloud computing environments (AWS, GCP).
- Prior experience in pricing, revenue assurance, media consumption patterns is valuable but not mandatory.
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