Architect - III

Position: Architect - III*
Description:

Remote Position

XXXX: XXXXXXXXXXX
40/hr.

Job Title:

Data Scientist

Organization:
Global Data & Digital Innovation (GDDI)

Overview

We are seeking a highly motivated Data Scientist to join the Global Data & Digital Innovation (GDDI) organization within the pharmaceutical commercial domain. This role focuses on building data science and AI-driven solutions, including predictive patient event modeling, Next Best Action (NBA) engines for HCP engagement, and GenAI-powered decision agents to enhance commercial effectiveness.

The ideal candidate will combine strong machine learning expertise with experience in GenAI agent development and scalable ML pipelines, enabling actionable insights for stakeholders across GDDI, Sales, Marketing, Sales Analytics, and Advanced Analytics functions.

Key Responsibilities

• Develop and deploy predictive models for patient events (line switches, initiation)

• Scale Next Best Action (NBA) solutions to optimize HCP engagement strategies across channels to various products

• Apply advanced ML techniques including regression, classification, and NLP techniques

• Create multi touch attribution pipelines for the customer journeys and optimization

• Integrate GenAI capabilities into commercial workflows such as:

• HCP engagement planning

• Content personalization

• Gen AI interfaces for ML pipelines

ML Engineering & Pipeline Development

• Oversee build and maintenance of end-to-end ML pipelines including:

• Data ingestion, feature engineering, model training, evaluation, and deployment

• Implement MLOps best practices:

• Model versioning, monitoring, and retraining pipelines

• CI/CD integration for scalable deployment

• Work with modern data platforms (e.g., Databricks, AWS)

Commercial Strategy & Stakeholder Support

• Partner with Sales, Marketing, and Sales Analytics teams to translate business problems into analytical solutions

• Deliver actionable insights and recommendations to senior stakeholders

• Collaborate with:

• Advanced Analytics teams (modeling and experimentation on alerts)

• Data Engineering teams (data pipelines and infrastructure)

• Business stakeholders (Sales, Marketing, Market Access)

• Act as a bridge between technical and business teams, ensuring adoption of advanced analytics and AI solutions

Data Management & Compliance