Manager, Data Analytics

WebMD is the most recognized and trusted brand of health information and the leading provider of health information services, serving consumers, physicians, healthcare professionals, employers and health plans through our public and private online portals and WebMD the Magazine.

The WebMD Health Network includes WebMD, Medscape, MedicineNet, eMedicine, RxList, theheart.org and Medscape Education.

Our consumer portals and mobile health applications provide engaging, relevant and credible health and wellness information, personalized health assessment tools and access to online communities.

WebMD is an Equal Opportunity/Affirmative Action employer and does not discriminate on the basis of race, ancestry, color, religion, sex, gender, age, marital status, sexual orientation, gender identity, national origin, medical condition, disability, veterans status, or any other basis protected by law.

WebMD is looking to hire an experienced statistician or data analyst with a strong understanding of pharmaceutical marketing programs.

The candidate will be responsible for hands-on development of various analytical models as well as for collaborating with external and internal analytical partners to measure impact of WebMD’s HCP and DTC campaigns and Medical Affairs programs.

The individual will serve various internal and external clients, and interact with multiple groups within the organization, including business intelligence, data services, and digital marketing product development.

We are looking for someone who is highly self-motivated, proactive and self-sufficient with the ability to juggle several projects and priorities on a regular basis while working independently, strategically and creatively.

The ideal candidate will have the following skillset:

• Foundational Skills : Strong problem solving and analytical skills, ability to think creatively with data, and ability to present analytical findings to clients at a level appropriate for the audience. Ability to develop analytical insights from secondary data analysis to support various purposes within the organization, including sales and campaign impact demonstration.
• Statistical Modeling : Knowledge of statistical techniques and concepts (regression, predictive modeling, power analysis, statistical tests and their proper usage, etc.) and experience with their applications.
• Causal Inference : Knowledge of causal inference using observational data. Ability to measurement framework, and experience in applying methods such as propensity score matching and difference-in-difference estimation.