Data Science Industrialization Lead, Scalable Ml Pipelines
hace 16 horas
Do you want to make a significant impact on patient health worldwide?
Pfizer Digital's Artificial Intelligence, Data, and Analytics organization (AIDA) is seeking a talented Data Science Industrialization Lead to leverage cutting-edge technology and inform critical business decisions.
We are at the forefront of Pfizer's transformation into a digitally driven organization, where data science and advanced analytics play a vital role in changing patients' lives. The Industrialization team within Enterprise Data Science and Advanced Analytics leads the scaling of data and insights capabilities, which are critical drivers and enablers of Pfizer's digital transformation.
Key Responsibilities:- Convert data/ML pipelines into scalable pipelines based on available infrastructure
- Enable production models across the ML lifecycle
- Determine model performance metrics and implement monitoring dashboards
- Determine and implement model retraining trigger mechanisms
- Design champion/challenger model and A/B testing automation
- Implement CI/CD orchestration for data science pipelines
- Manage production deployments and post-deployment model lifecycle management activities: drift monitoring, model retraining, and model technical evaluation & business validation
- Work with stakeholders to assist with ML pipeline-related technical issues and support modeling infrastructure needs
- Partner with AIDA Data team to integrate developed ML pipelines into enterprise-level analytics data products where appropriate
- Partner with AIDA Platforms team on continuous development and end-to-end capability integration between out-of-box platforms and internal engineered components (API registry, ML library/workflow management, enterprise connectors); Performance and resource optimization of managed pipelines and models
Qualifications
- Must-Have:
- Bachelor's degree in ML engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
- 5+ years of work experience in data science, analytics, or engineering for a diverse range of projects
- Understanding of data science development lifecycle (CRISP)
- Strong hands-on skills in ML engineering and data science (e.g., Python, R, SQL, industrialized ETL software)
- Experience working in a cloud-based analytics ecosystem (AWS, Snowflake, etc)
- Highly self-motivated to deliver both independently and with strong team collaboration
- Ability to creatively take on new challenges and work outside comfort zone
- Strong English communication skills (written & verbal) - Nice-to-Have:
- Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems or related discipline
- Experience with data science enabling technology, such as Data Science Studio or other data science platforms
- Hands-on experience working in Agile teams, processes, and practices
- Understanding of MLOps principles and tech stack (e.g., MLFlow)
- Experience in CI/CD integration (e.g., Git Hub, Git Hub Action or Jenkins)
- Experience in software/product engineering
- Strong hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 9 or 10) platform
- Pharma & Life Science commercial functional knowledge
- Pharma & Life Science commercial data literacy
- Experience with Dataiku Science Studio
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