Pfizer

Makes cancer drugs, vaccines and other medicines

Data Science Industrialization

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeThessaloniki, GreeceNo salary listed

About the Job

Requirements

A bachelor's degree in a machine learning engineering-related area such as Data Science, Computer Engineering, Computer Science, Information Systems, Engineering, or a related discipline is required.
At least 2 years of work experience in data science, analytics, or engineering across a diverse range of projects is required.
Understanding of the data science development lifecycle, including CRISP, is required.
Hands-on skills in machine learning engineering and data science, including Python, R, SQL, and industrialized ETL software, are required.
Ability to deliver independently and collaborate effectively with a team is required.
Ability to take on new challenges and work outside one's comfort zone is required.
Strong written and verbal English communication skills are required.

Responsibilities

Convert data and machine learning pipelines into scalable pipelines using available infrastructure, such as converting Python-based data science code into PySpark or SQL for scalable pushdown execution.
Enable production models across the machine learning lifecycle.
Implement model performance metrics and model monitoring dashboards.
Implement model retraining trigger mechanisms.
Implement champion/challenger model and A/B testing automation.
Implement continuous integration and continuous delivery orchestration for data science pipelines.
Manage production deployments and post-deployment model lifecycle activities, including drift monitoring, model retraining, and model technical evaluation and business validation.
Work with stakeholders to address machine learning pipeline-related technical issues and support modeling infrastructure needs.

Desired Qualifications

An advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems, or a related discipline is preferred.
Experience with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker, or other data science platforms, is preferred.
Hands-on experience working in Agile teams, processes, and practices is preferred.
Understanding of MLOps principles and technology stacks, such as MLflow, is preferred.
Experience with continuous integration and continuous delivery integration tools such as GitHub, GitHub Actions, or Jenkins is preferred.
Experience working in a cloud-based analytics ecosystem such as AWS or Snowflake is preferred.

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 Engineer, 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
Implement model performance metrics and model monitoring dashboards
Implement model retraining trigger mechanisms
Implement 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

Qualifications

Must-Have

Bachelor’s degree in ML engineering related area (Data Science, Computer Engineering, Computer Science, Information Systems, Engineering or a related discipline)
2+ years of work experience in data science, analytics, or engineering for a diverse range of projects
Understanding of data science development lifecycle (CRISP)
Hands-on skills in ML engineering and data science (e.g., Python, R, SQL, industrialized ETL software)
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 Dataiku Data Science Studio, AWS SageMaker 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. GitHub, GitHub Actions or Jenkins)
Experience working in a cloud based analytics ecosystem (AWS, Snowflake, etc)

Work Location Assignment: Flexible

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.

Information & Business Tech#LI-PFE

About the Company

Pfizer

Makes cancer drugs, vaccines and other medicines

Pfizer discovers, makes, and sells prescription medicines and vaccines for patients, doctors, and governments worldwide, with large businesses in vaccines, cancer, heart disease, and rare diseases. In 2020, its COVID-19 shot, developed with German partner BioNTech, became the first COVID-19 vaccine authorized in the United States. Its scientists find drug candidates and run clinical trials to prove they are safe and effective, and Pfizer then manufactures approved products in its own plants, earning most of its money from patented drugs before cheaper generic copies arrive. Pfizer was founded in 1849 as a chemicals business in Brooklyn and is based in New York City.

Company size: 10,001+
Company stage: IPO
Headquarters: New York City, New York
Founded: 1849

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