Director, DS&AI – Predictive Modeling & Personalization Signals

Financecolombia; Barcelona; Presencial; EUR 85.000 - 120.000; Jornada completa

Descripción de la vacante

A leading pharmaceutical company in Barcelona is seeking a Director to develop predictive models that enhance HCP engagement strategies. The ideal candidate will have a strong background in machine learning, statistics, and real-world healthcare applications. This role offers an engaging work environment with a focus on flexibility and collaboration. Ready to make an impact? Apply now!

Formación

• MS or PhD or equivalent experience in Machine Learning or Statistics.
• Experience applying ML in real-world healthcare or customer targeting.
• Familiarity with claims, EMR, CRM, and digital engagement data.

Responsabilidades

• Build predictive models to detect care gaps and forecast behaviors.
• Collaborate with teams to ensure engagement decisions are data-driven.
• Implement explainability techniques for model transparency.

Conocimientos

Machine Learning

Statistics

Data Science

Healthcare Experience

Personalization Logic

Educación

MS or PhD in Machine Learning or related field

Descripción del empleo

Director, DS&AI – Predictive Modeling & Personalization Signals

Introduction to role:

Are you ready to develop predictive models that serve as the intelligence backbone for AI agents? Dive into a role where you'll power field strategy, content sequencing, and personalized HCP engagement. You'll enable N=1 care gap detection, real-time HCP targeting, and personalized content triggering using data-rich, explainable ML frameworks. Are you up for the challenge?

Responsibilities

• Build predictive models to detect care gaps, forecast treatment behavior, and prioritize HCP outreach.
• Collaborate with field and content teams to ensure models fuel the right engagement decisions at the right moment.
• Implement explainability techniques (e.g., SHAP, LIME) to ensure model transparency for commercial, medical, and legal review.
• Tune models for real-time feedback from AI agents and CRM behavior data.

Qualifications

• MS or PhD or equivalent experience in Machine Learning, Statistics, or Applied Data Science.
• Experience applying ML in real-world healthcare or customer targeting contexts.
• Familiarity with claims, EMR, CRM, and digital engagement data.
• Strong understanding of personalization logic and treatment journey modeling.

When we put unexpected teams in the same room, we unleash bold thinking with the power to encourage life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our outstanding and ambitious world.

At AstraZeneca, you'll find yourself at the heart of a transformative journey. We connect across the business to influence patient outcomes and improve lives. Our dedication to innovation means you'll work alongside leading experts, using ground breaking techniques to turn complex information into practical insights. Here, your contributions are valued and recognized as we drive exciting transformation towards becoming a digital and data-led enterprise.

Ready to make an impact?

Apply now and be part of our journey!

Date Posted 11-Sept-2025

Closing Date 01-Oct-2025

AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.