Senior Data Scientist - Patient Access

Company Description

AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.

Job Description

The Data Scientist is the primary driver behind advanced analytics supporting AbbVie Complete Access processes. A successful Data Scientist will translate business needs into analytical questions, conduct data exploration and model specification, design and perform rigorous analyses of operational, patient/customer, healthcare provider, and financial data, and translate these analytical findings into leading information for our business partners. The chosen candidate will act as an advisor for executive and management-level decision makers. This individual will also provide direction to IT on data governance and industry best practices, with a focus on agility and efficiency.

This position is based in Abbott Park in North Chicago, IL (3 days onsite/2 days remote, 100% remote option is not available), relocation assistance is not available for this role. 

Responsibilities:

• Spearhead analytical initiatives that drive operational efficiency and patient engagement improvements. Help lead the development and calculation of metrics measuring the health of brand/program planning, demand forecasting, inventory management, patient engagement, and overall business delivery goals.
• Produce innovative analytic solutions that drive existing and changing patient access processes. Develop predictive algorithms using statistical learning methods to solve complex business questions.
• Be a thought leader in the application of cutting-edge predictive methods (e.g., neural networks, computer vision, etc.) and the technology needed to support them.
• Utilize operational analytics and project management tools to optimize the execution of analytic initiatives and studies, manage internal and external resources,