Associate Director, Real-world Data Analytics and Innovation at Merck & Co, Inc in Kenilworth, NJ

Who you are:

You are a seasoned data scientist and innovator with interest and experience in Real-World Evidence (RWE) generation through advanced analytics and innovative approaches. You have a strong desire to influence internal and external decision making through the application of innovative data science and technology.

Where you fit in:

You will collaborate with and support a diverse set of scientists in the Center for Observational and Real-world Evidence (CORE) to deliver analytics expertise in support of outcomes studies, develop advanced, strategic real-world data analytics capabilities to support market access, reimbursement and other business imperatives. You will lead the design and implementation of the company's Real-world data initiatives in close collaboration with internal and external stakeholders. You will help identify business and scientific opportunities as you broaden the scope of collaboration with other functions in the Research Division and Human Health. You will have the opportunity to collaborate with IT professionals as you seek to turn meaningful findings into solutions that may be used in care management and clinical decision support. You will do this in a vibrant research environment, with a strong innovative culture and at a national center of the health informatics. You will have access to a wealth of resources and growth opportunities that a large pharmaceutical company can offer.

Responsibilities

• Partner with analytics leads and cross-functional stakeholders to deliver actionable insights from real-world data in support of the product line Value Evidence strategy.
• Drive RWD quality assessments to inform investment decisions and deliver standardized, analysis-ready patient cohorts to generate RWE and improve patient outcomes.
• Support the development of real-world data, analytics and platform capabilities, applying cutting-edge advanced analytics methodologies and tools to address scientific questions related to health economic resource utilization and clinical outcomes.
• Propose, design and execute outcomes research studies from a data science and analytics perspective.
• Ensure high quality, rigorous and readily interpretable deliverables from RWE studies/analyses.
• Design self-service analytics tools to enhance efficiencies and expand the use of RWD across the product life-cycle.
• Effectively communicate novel findings and methodological approaches to internal and external stakeholders
• Drive innovation in methods and algorithms to accelerate and improve evidence delivery.

Qualifications

Education Requirement:

• Training in data science, biomedical informatics, health informatics, clinical informatics, epidemiology, biostatistics, health economics and outcomes research, or a related field required; a Master degree with at least 6 years relevant work experience is required.

Required Experience and Skills**:

• Six or more years of experience in analyzing healthcare administrative data, electronic health records, and/or disease/patient registries for relevant quantitative and qualitative research.
• Broad understanding and application of advanced statistical and predictive modeling methods for RWE generation.
• Broad understanding and application of machine learning algorithms (supervised, unsupervised learning, deep neural network, transfer learning) in biomedical research and/or healthcare settings
• Experience with advanced information technology (e.g. cloud computing, high-performance computing)
• Good communication and interpersonal skills
• Healthcare or pharmaceutical industry experience and business acumen

Preferred Experience and Skills:

• Experience with the whole life cycle of rea-world evidence and outcomes studies (planning, generation, and dissemination)
• Exposure with payer analytics and/or HTA methodologies for value-based contracting
• Experience with Big Data technology (such as AWS Redshift)
• Exposure/familiarity with cognitive computing/artificial intelligence
• Exposure/familiarity in Natural Language Process (NLP) and social media data mining