Data Science Industrialization, Ml Workflow

hace 5 horas

San José, Costa Rica Pfizer A tiempo completo

Role Summary

Do you want to make an impact on patient health around the world?
Do you thrive in a fast-paced environment that brings together scientific, clinical and commercial domains through engineering, data science, and analytics?
Then join Pfizer Digital's Artificial Intelligence, Data, and Analytics organization (AIDA) where you can leverage cutting-edge technology to inform critical business decisions and improve customer experiences for our patients and physicians.
Our collection of engineering, data science, and analytics professionals are at the forefront of Pfizer's transformation into a digitally driven organization leveraging data science and advanced analytics to change patient's lives.
The Industrialization team within Enterprise Data Science and Advanced Analytics leads the scaling of data and insights capabilities - critical drivers and enablers of Pfizer's digital transformation.
Role Responsibilities
- Convert data/ML pipelines into scalable pipelines based on the infrastructure available (e.g.
convert Python based data science code into PySpark/SQL for scalable pushdown execution)
- 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 the 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 OOB 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

LI-PFE

Work Location Assignment: Flexible

Pfizer is an equal opportunity employer and complies with all applicable equal employment opportunity legislation in each jurisdiction in which it operates.
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