Senior Associate, AI and Data Science Full Stack Engineer @ Pfizer

Do you want to make a global impact on patient health? Do you thrive in a fast-paced environment that integrates scientific, clinical, and commercial domains through engineering, data science, and AI? Join Pfizer Digital s Artificial Intelligence, Data, and Advanced Analytics organization (AIDA) to leverage cutting-edge technology for critical business decisions and enhance customer experiences for colleagues, patients, and physicians. Our team of engineering, data science, and AI professionals is at the forefront of Pfizer s transformation into a digitally driven organization, using data science and AI to change patients lives. The Data Science Industrialization team is a key driver of Pfizer s digital transformation, leading process and engineering innovations to advance AI and data science applications from prototypes and MVPs to full production.

As a Sr. Associate, AI and Data Science Full Stack Engineer , you will join the Data Science Industrialization team. Your responsibilities will include implementing AI solutions at scale for Pfizer business. You will iteratively develop and continuously improve data science workflows, AI based software solutions and AI components.

ROLE RESPONSIBILITIES

• Contribute to the end-to-end build and deployment of data science and analytics products and AI modules

• Develop server-side logic using back-end technologies such as Python

• Contribute to the implementation of data ETL pipelines using Python and SQL

• Build web applications with JavaScript frameworks

• Build data visualizations and data applications to enable data exploration and insights generation

• Maintain infrastructure and tools for software development and deploying using IaC tools

• Automate processes for continuous integration, delivery, and deployment (CI/CD pipeline) to ensure smooth software delivery

• Implement logging and monitoring tools to gain insights into system behavior.

• Collaborate with data scientists, engineers, and colleagues from across Pfizer to integrate AI and data science models into production solutions

• Build data visualizations and data applications to enable data exploration and insights generation (e. g. Tableau, Power BI, Dash, Shiny, Streamlit, etc. ).

• Stay up-to-date with emerging technologies and trends in your field.

BASIC QUALIFICATIONS

• Bachelors or Masters degree in Computer Science, or a related field (or equivalent experience).

• 2+ years of experience in software engineering, data science, or related technical fields.

• Experience in programming languages such as Python or R

• Familiarity with back-end technologies, databases (SQL and NoSQL), and RESTful APIs.

• Familiarity with data manipulation and preprocessing techniques, including data cleaning, data wrangling and feature engineering.

• Highly self-motivated, capable of delivering both independently and through strong team collaboration.

• Ability to creatively tackle new challenges and step outside your comfort zone.

• Strong English communication skills (written and verbal).

PREFERRED QUALIFICATIONS

• Advanced degree in Data Science, Computer Engineering, Computer Science, Information Systems or related discipline

• Experience in CI/CD integration (e. g. GitHub, GitHub Actions) and containers (e. g. docker)

• Understanding of statistical modeling, machine learning algorithms, and data mining techniques.

• Experience with data science enabling technology, such as Dataiku Data Science Studio, AWS SageMaker or other data science platforms

• Knowledge about BI backend concept like Star Schema and Snowflake

• Familiarity in building low code dashboard solution tools like Tableau, Power BI, Dash and Streamlit

• Experience developing dynamic and interactive web applications; familiar with React, AngularJS, Vue.

• Experience with Infrastructure as Code (IaC) tools such as Terraform, Ansible, or Cloudformation

• Familiarity with cloud-based analytics ecosystems (e. g. , AWS, Snowflake).

• Hands on experience working in Agile teams, processes, and practices

Ability to work non-traditional work hours interacting with global teams spanning across the different regions (eg: North America, Europe, Asia)