Sr. Manager - Data Scientist

The person in this role is accountable for delivering data science-driven

strategic and tactical support in China. As a strategic partner to Commercial

and other function teams, this person will develop and implement models and

data-science derived insights that directly influence and transform most

critical questions across the business. This will include leading the execution

and interpretation AI/ML models, framing problems, and shaping solutions with

clear and compelling communication of data-driven insights.

As a Senior Manager Data Scientist, the person in this role will work closely

with the Data Science COE and shape the industrialization of bespoke AI/ML

models, that directly impact the working models of other members of the GCA

Data Science Team.

This role is dynamic, fast-paced, highly collaborative, and covers a broad

range of strategic topics that are critical to our business. The successful

candidate will join GCA colleagues worldwide that are constantly supporting

business transformation through their proactive thought leadership, innovative

analytical capabilities, and their ability to communicate highly complex and

dynamic information in new and creative ways.

POSITION RESPONSIBILITIES

Key Roles & Responsibilities:

Commercial and Medical Data Science and Insights

* Provide data science and insights to Country Commercial and Medical teams

to drive brand tactic decisions

* Act as strategic partner to frame, investigate, translate complex data

related models, and answer key business questions related to the identification

and evaluation of Commercial and Medical brand strategies and tactics

* Lead the development and execution of data science projects, including the

design and implementation of machine learning models and algorithms

* Oversee the collection, processing, and analysis of large datasets to

extract actionable insights

* Ensure the quality and accuracy of data and analytics outputs through

rigorous testing and validation

* Interface with Omnichannel-operations on execution and data science bespoke

analytical needs

* Connect machine learning models and insights together to identify Medical

and Commercial brand opportunities and tactics to execute

* Guide Medical and Commercial brand teams via compelling and persuasive

story and deliver clear and actionable brand tactic recommendations

* Track and analyze impacts of Medical and Commercial brand strategies and

tactics using dashboards and data products

* Stay up-to-date with the latest advancements in data science and machine

learning technologies, LLMs and Generative AI

Collaborate with Data Science COE

* Partner with Data Science COE to develop bespoke AI and machine learning

models; Configure pre-built models and interpret updated results

* Ensure alignment with Data Science COEs teams to ensure cohesive activities

with stakeholders

Collaboration with other GCA and Analytics teams

* Partner with GCA market lead to incorporate and prioritize data science

insights into analytics plans and recommendations

* Contribute to the advancement of GCA data science and consulting

capabilities; seek to share knowledge and expertise with other colleagues

making use of knowledge sharing platform

* Partner with other analytic functions to advance the use of novel data

sources, including RWD

* Design and implement secure and scalable infrastructure tools, data

integrations, and automation using modern light-weight technologies that enable

data engineering (content development) to create scaled analytics.

* Collaborate with cross-functional teams including Pfizer Enterprise IT to

align, standardize, optimize, and scale infrastructure in alignment with

Pfizer's standards.

* Own and refine existing continuous integration continuous delivery (CI/CD)

processes for build, test, and deployment within the Commercial Data Science

platform stack.

* Work closely with content development teams to streamline workflows and

improve efficiency of tools for their use in the local development context,

following SDLC best practices.

* Innovate to solve complex problems that require knowledge of containers,

APIs, AWS and Azure cloud infrastructure, security, and best practices in

infrastructure engineering.

* Develop monitoring solutions and operational process for analytics

platforms, ensuring early detection and rapid response to potential issues.

* Participate in incident response activities, troubleshoot problems, and

implement preventive measures such as expansion of QA frameworks.

* Collaborate with Data Science and Data Science teams to understand

application requirements and provide effective infrastructure support.

* Maintain comprehensive documentation of infrastructure configurations and

processes, including the DevOps runbook.

* Own coding and engineering best practices standards and governance for a

multi-faceted set of ELT pipelines that operate on various cadences to satisfy

business requirements.

* Implement security best practices to protect analytics environments and

ensure compliance with Pfizer’s stringent standards.

* Lead and build test automation tools and frameworks to test pipelines.

* Partner with enterprise Digital Engineering team to identify or code APIs

that need to be instrumented for data analytics and reporting and align with

already established data pipelines.

Domain Knowledge and Standards

* Ramp up quickly in understanding data sources required for data products

and the goals of Commercial Analytics in a Pharma business.

* Collaboration with the Data Science Director to work hand in hand with the

Platform/Digital and DevOps team to specify platform requirements, tool

enhancements, observability & monitoring, security/audit, and alerting on

various data pipelines and jobs as needed for operation the Commercial Data

Science organization.

ORGANIZATIONAL RELATIONSHIPS

Reports to:

* Solid Line to: Director, China Data Science & AI Lead

RESOURCES MANAGED

EDUCATION AND EXPERIENCE

Education: Bachelor’s degree required, Masters in analytic discipline/

statistics preferred

Qualifications / Experience:

* Master’s or Ph.D. in Data science, computer science, statistics,

engineering, data science, or related applicable field.

* 7+ years of experience using data science or advanced analytics solving

real business problems, working in an agile development team to design and

develop machine learning and AI solutions.

* Recent Healthcare Life Sciences (pharma preferred) and ecosystem

professional industry experience is preferred, commercial/marketing experience

is a plus.

TECHNICAL SKILLS REQUIREMENTS

* Strong proficiency in programming languages such as Python, SQL, and/or R.

* Experience with matching learning frameworks and tools such as TensorFlow,

Pytorch, and Scikit-learn.

* Knowledge and practical experience of Statistical methods and A/B testing

experimentation method.

* Knowledge and practical experience of Machine learning, Deep learning, and

Time series prediction.

* Solid understanding of data management and big data processing technologies

(e.g., Apache Spark, Schema design, Data Pipeline, Workflows).

* Strong data storytelling and stakeholder management abilities.

* Excellent problem-solving skills and the ability to think critically and

analytically.

* Professional hands-on experience with containers (e.g., Docker, ECS,

Kubernetes), cloud platforms (e.g., AWS, Azure), and event-driven architecture

is required.

* Advanced experience working in multi-stage environments that leverage

automation such as Step Functions, Airflow, Cron, etc.

* Must demonstrate ability to leverage digital diagraming tools and

communicate technical requirements in a remote setting.

* Must have experience working in Jira and Confluence, and be a strong

writer, contributing to engineering team documentation/playbooks.

* Experience in project management and stakeholder engagement to drive impact.

* Experience in developing QA automation frameworks, functional and

non-functional, is a plus.

* Experience with cloud platforms such as AWS, Azure, or Dataiku is a plus

Preferred Skills:

* Knowledge of containerization and orchestration tools such as Docker and

Kubernetes.

* Experience with big data technologies such as Snowflake, Hadoop, Spark, and

Hive.

* Familiarity with data visualization tools such as Tableau , Power BI,

Webapp.

* Experience with large language models (LLMs) and generative AI technologies.

Competencies

* Project Management: Overseeing complex, cross-functional projects, ensuring

delivery on time and within budget.

* Technical Strategy: Contributing to the technical strategy and architecture

decisions, ensuring scalability and performance of data solutions.

* Collaboration: Influencing their data engineering team, working closely

with other departments, understanding their data needs, and delivering

solutions that meet these needs.

* Operational Excellence: Ensuring the reliability, efficiency, and quality

of data services andpipelines.

* Change Management: Leading change initiatives, improving processes, and

implementing new technologies.

Candidate demonstrates a breadth of diverse leadership experiences and

capabilities including: the ability to influence and collaborate with peers,

develop and coach others, oversee and guide the work of other colleagues to

achieve meaningful outcomes and create business impact

PHYSICAL POSITION REQUIREMENTS

Physical location: Beijing/Shanghai

Work Location Assignment: On Premise

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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