Measurement Science Lead

• →Lead causal inference and impact measurement initiatives to evaluate the effectiveness of commercial and customer engagement strategies.
• →Apply advanced statistical methods and experimentation to quantify incremental business impact and inform decision-making.
• →Develop attribution frameworks across field and digital customer engagement channels.
• →Partner with business teams to translate commercial questions into actionable insights.
• →Mentor data scientists and champion best practices in causal analytics and measurement.

As we work to develop treatments that take care of others, we also work to care for our teammates’ professional and personal growth and well-being.

You will be part of a collaborative analytics environment where data science is used to improve commercial decision-making, strengthen customer engagement, and help teams better understand patient and HCP needs.

• 5–7 years of experience in causal inference, experimentation, marketing science, or data science.
• Strong expertise in causal inference, statistical modeling, experimental design (A/B testing), and quantifying business impact.
• Experience applying quasi-experimental methods (e.g., propensity score matching, difference-in-differences, synthetic controls, uplift modeling) to evaluate commercial initiatives.
• Proficiency in Python, SQL, and large-scale data environments for experimental analysis and causal measurement.
• Ability to translate complex statistical findings and causal insights into actionable recommendations for business stakeholders.

• Experience in life sciences, healthcare, or pharmaceutical analytics.
• Experience in commercial analytics