Senior Data Scientist
Merck Sharp & Dohme Corp.
Singapore, Singapore, SG
31d ago

Description

The Senior Data Scientist will use information and models to gain insight into business performance, support fact-based decisions, and communicate opportunities for sustained improvement.

More specifically the Senior Data Scientist / Predictive Modeller will apply machine learning and statistical modelling methodologies to build and maintain the predictive models on product market dynamics, population, revenue, and product launches and other market events.

You will be part of the effort to build products that support the decision makers in general management, finance and commercial departments.

Key responsibilities, ensuring the regular on-time operations on client support, code maintenance and continuous improvement.

In addition, co-leading the collaboration related to the front end development, i.e. work related to visualization, dashboard and data

Primary Activities

  • Communication effectively across different stakeholders
  • Understanding of the business context of the decisions to be supported, helping to interpret results and ensure high level adoption
  • Ensuring quality on-time delivery according to the standards and best practices methodology
  • Managing the backlog and risks during the modelling
  • Suggesting the appropriate mathematical approach
  • Driving the development of the SW code (ensure quality, documentations, reproducibility and reusability) to execute the predictive models
  • Hands-on use of R to design and develop analytical models for advanced techniques such as forecasts, simulations, and optimizations
  • Assess Analytical outputs and use the data to draw conclusions, identification of options, and making recommendations that aid in realization of value
  • Continuously seek to embed analytical tools within business processes
  • Recognition of the repeatable situation and suggesting the appropriate level of automation of analytics
  • Lead maintenance and improvement of model performances, and identification of additional use cases working with stakeholders
  • Building and maintaining of knowledge on the data sources, the data quality and metadata
  • Co-Lead efforts to improve the front end applications i.e. visualization, dashboard and data management and integration.
  • People management and talent development for team of 2-4 data science practitioners
  • Promotion of a culture of modelling and analytics as a differentiator and competitive advantage an environment that places high value on embedding analytical tools within business processes, and using information to make fact-based decisions
  • Stay in touch with modern methodology within predictive modelling
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