Data Scientist

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

Work with large-scale healthcare datasets such as:

Claims, EHR/EMR, CRM, and digital engagement data

Ensure compliance with data privacy and regulatory standards (e.g., HIPAA)

Required Qualifications