Data Science Industrialization, Machine Learning Engineering Manager
Pfizer Inc.
Thessaloniki Chortiatis, Greece
Job posting number: #7113808 (Ref:pf-4863874)
Posted: October 13, 2022
Application Deadline: Open Until Filled
Job Description
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.
As an ML Engineering Manager, you will be part of the Data Science Industrialization team charged with building and automating high quality data science pipelines that power key business applications with advanced analytics/AI/ML. You will be a member of a global team that defines and maintains ML Ops best practices and deploys and maintains production analytics and data science modeling workflows.
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
Work Location Assignment: Flexible
LI#PFE
Purpose
Breakthroughs that change patients' lives... At Pfizer we are a patient centric company, guided by our four values: courage, joy, equity and excellence. Our breakthrough culture lends itself to our dedication to transforming millions of lives.
Digital Transformation Strategy
One bold way we are achieving our purpose is through our company wide digital transformation strategy. We are leading the way in adopting new data, modelling and automated solutions to further digitize and accelerate drug discovery and development with the aim of enhancing health outcomes and the patient experience.
Flexibility
We aim to create a trusting, flexible workplace culture which encourages employees to achieve work life harmony, attracts talent and enables everyone to be their best working self. Let’s start the conversation!
Equal Employment Opportunity
We believe that a diverse and inclusive workforce is crucial to building a successful business. As an employer, Pfizer is committed to celebrating this, in all its forms – allowing for us to be as diverse as the patients and communities we serve. Together, we continue to build a culture that encourages, supports and empowers our employees.
Pfizer is committed to equal opportunity in the terms and conditions of employment for all employees and job applicants without regard to race, color, religion, sex, sexual orientation, age, gender identity or gender expression, national origin, disability or veteran status. Pfizer also complies with all applicable national, state and local laws governing nondiscrimination in employment as well as work authorization and employment eligibility verification requirements of the Immigration and Nationality Act and IRCA. Pfizer is an E-Verify employer.