Pfizer’sChief Digital Office (CDO) leads the transformation of Pfizer into a digital powerhouse that will generate patient superior experiences which results in better health outcomes.

The Analytics Experienceteam, which is part of the Analytics & Dataorg within Digital Health, Medicines, and Artificial Intelligence,is responsible for the creating a seamless experience for analytics experts to harness the potential of big data, machine learning, and interactive analytics through a unified platformacross the enterprise from scientific / clinical to commercial across all Pfizer geographies.

Part of the Analytics Experience team is anAnalytics Extensibility teamthat will be responsible fordeveloping frameworks and tools to industrialize data science processes and enabling analytics experts to deploy machine learning models into production using the enterprise analytics platform, Pfizer Insights.

Pfizer Insights will be thedigital engine that brings together investments we have made into a unified experience for colleagues that take us to the next level of value creation.

It powers next generation insights by developing enterprise-grade data foundations, allowing data to flow horizontally, enabling a versatile analytics environment, and embedding insights into day-to-day work to create a digital, data-driven culture.

The role will be responsible for developing and maintaining a machine learning operations (MLOps) foundation and framework as part of an enterprise analytics platform, Pfizer Insights.

This role with partner with data scientists from Medical and Commercial to understand and transform machine learning models developed to solve critical business outcomes into production grade for deployment into end user sales and marketing (MarTech) solutions.

The role will be interfacing directly with both Digital and Business data engineer, data science, and product teams to understand requirements and develop assets in close collaboration with users.

You will have the ability to work with talented engineers, embrace uncertainty, and invent a model for enriching analysts platform experience.

The person in this position will need managea featureroadmap tools and frameworks in partnership Analytical Experience leadsand other Data and Analytics teams to ensure identified value can be developed and delivered.

Tough decisions will need to be made to balance the needs of multiple, differing stakeholders with competing priorities.

ROLE RESPONSIBILITIES

Mining massive amounts of real-world healthcare, research, and business data to extract useful insights

Capturing and analyzing data from new and novel data sources

Provide best practices, guidance, and support to data science team : Data versioning, Model tracking, Experiment tracking and Code bundling for ease-of-deployment

Assist in creation and conversion of developed 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)

Develop continuous monitoring & training pipelines allowing model training in production by collaborating with data science team

Determine model performance monitoring metrics

Determine retraining trigger mechanism

Design champion / challenger model and A / B Testing

Develop CI / CD orchestration for the data science training pipeline

Manage the production deployments and post deployment model lifecycle management activities : Drift monitoring, Model retraining, and Model technical evaluation & business validation

Create parameterized data science pipeline for reuse across brands

Create modularized data science widget to be used across commercial analytics & analysis

SME for data science on matrix work teams

Able to work in ambiguous projects and deliver analytical framework

Develops project plan including timelines and milestones

BASIC QUALIFICATIONS

BS in computer science, data science, / or an engineering / quantitative field.

5-8 years of experience in data and analytics field

Experience / projects involving real-world analytical problems using machine learning, statistical modeling, or similar quantitative approaches

Fluency in SQL or other programming languages (Python, Java, and / or C++)

Some development experience in at least one scripting language (PHP, Perl, Python, etc.)

Some understanding of MLOps principles and tech stack (e.g.,MLFlow)

Some understanding of CI / CD integration and the data science development lifecycle

Strong communication skills (written & verbal)

Hands-on skills for data and machine learning pipeline orchestration via Dataiku (DSS 9 or 10) platform or similar platform

Project management experience

A passion and proven ability for problem-solving, comfort with ambiguity, and creativity

Ability to thrive in a dynamic and fast-paced environment and drive change, and collaborate effectively with a variety of individuals and organization

PREFERRED QUALIFICATIONS

Pharma & Life Science commercial functional knowledge is a plus

Pharma & Life Science commercial data literacy is a plus