Promotion Optimization Specialist Job

Employer
Merck
Location
Gwynedd, PA, US
Posted
October 12 2017
Discipline
Other
Organization Type
Pharma
Requisition ID: QUA006258

Merck & Co., Inc. Kenilworth, N.J., U.S.A. known as Merck in the United States and Canada, is a global health care leader with a diversified portfolio of prescription medicines, vaccines and animal health products. The difference between potential and achievement lies in the spark that fuels innovation and inventiveness; this is the space where Merck has codified its legacy for over a century. Merck’s success is backed by ethical integrity, forward momentum, and an inspiring mission to achieve new milestones in global healthcare.



Merck’s Global Human Health (GHH) Division abides by a “patient first, profits later” ideology. Results-driven and ambitious, this team of individuals represents a functional balance between meeting company objectives and the needs of people around the world. The organization is comprised of sales and marketing professionals who are passionate about their role in bringing Merck's prescription medicines, vaccines, and other medical products to our customers worldwide. 





The position of Promotion Optimization Specialist requires the development of data-driven, profit-maximizing recommendations concerning the allocation and targeting of promotional resources through the application of quantitative methods to secondary data sources.


Primary activities and responsibilities include, but are not limited to:




  • Promotion response modeling  and Promotional resource allocation assessments

  • Impact assessment for physician- and patient-directed promotional programs 

  • Market Mix analysis

  • Return-on-investment analysis 

  • Behavioral segmentation

  • Responsible for all phases of planning & executing projects assigned by manager. 

  • Ability to work autonomously on a project

The Primary Activities include:


  • Directly influence decisions concerning the amount, allocation and targeting of promotional resources 

  • Projects are product-specific, including new and in-line products, and/or focused on issues spanning multiple products 

  • Challenged to synthesize information about therapeutic markets and their products, current marketing and sales practices, best practice marketing concepts, and pertinent market data to develop actionable promotion resource allocation recommendations

This position resides within the Promotion Optimization team within the Investment Analytics & Decision Sciences organization of US Market Operations and Strategy Realization.



Qualifications


Education: 


  • Quantative Science Degree (BA / BS)

Required Experience and Skills**: 


  • As a fair portion of the role will also require the development of hands on analytics and predictive solutions, the candidate must have a degree in Statistics, Engineering, Mathematics, Operation Research, or any Quantitative Decision field. 

  • Working knowledge of SAS, R and Excel are required. 

  • The candidate must have a minimum of three years of experience in developing and applying analytics solutions along with managing the projects and client communications to solve business challenges related to Marketing and/or Sales in the pharmaceutical industry

  • Understanding of the Health Care or Pharmaceutical industry and experience in using various 3rd party data sources, such as IMS Exponent and/or Longitudinal Patient Level Data are necessary. 

  • Good communication and leadership skills are critical in order to develop, propose and convey technical concepts to business customers and executives for improved promotion allocation.

  • Ability to develop concise presentations with clear recommendations to inform decisions made by Senior Leaders. 

  • The candidate must also have demonstrated good client and project management experience, having to manage multiple analytical projects simultaneously and foster collaboration with colleagues. 



Preferred :


  • Advanced degree (MS or above) in Statistics or Engineering or Operation Research

  • Experience with Python, SQL and various analytical and data mining tools

  • Experience in applying linear and non-linear optimization techniques to address business questions.

  • Experience in developing and applying metrics related to health care consumer’s medication affordability, adherence and abandonment using longitudinal patient level data.

  • Experience with one or more of the following advanced techniques is also desirable:  Bayesian data analysis, longitudinal analysis of time series cross sectional data, repeated measures modeling, Hierarchical Linear Modeling, data mining techniques, Classification and Regression Trees (CART)/ Chi-squared Automatic Interaction Detector (CHAID), and/or Discrete Choice Models.







Your role at Merck is integral to helping the world meet new breakthroughs that affect generations to come, and we’re counting on your skills and inventiveness to help make meaningful contributions to global medical advancement. At Merck, we’re inventing for life. 


 


If you need an accommodation for the application process please email us at staffingaadar@merck.com.


  


Search Firm Representatives Please Read Carefully: 


Merck & Co., Inc. is not accepting unsolicited assistance from search firms for this employment opportunity.  Please, no phone calls or emails.  All resumes submitted by search firms to any employee at Merck via email, the Internet or in any form and/or method without a valid written search agreement in place for this position will be deemed the sole property of Merck.  No fee will be paid in the event the candidate is hired by Merck as a result of the referral or through other means.


 


 


 


For more information about personal rights under Equal Employment Opportunity, visit:


 


            EEOC Poster


            EEOC GINA Supplement 



Job: Quantitative Sciences Generic
Other Locations:
Employee Status: Regular
Travel: Yes, 5 % of the Time
Number of Openings:
Shift (if applicable):
Hazardous Materials:
Company Trade Name: Merck

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