About The Position

Lead and execute clinical and operation advanced analytics or system improvements. Develop novel data science methodologies to generate deep insights from disparate data sources. Provide subject matter expertise to teams in identification and application of the right data science methodology for a business problem. Formulate the right business problem in collaboration with business and create necessary analysis plans. Develop machine learning methodologies both supervised and unsupervised as appropriate to the business problem. Develop scalable and robust code in relevant language such as R or Python in support of data science projects. Perform exploratory data analysis, system design analysis, integration design analysis, database design analysis to generate insights and help answer business questions. Ensure the life cycle management of the models is maintained through code repositories and version control tools. Perform data engineering, data preprocessing and data wrangling activities utilizing different approaches such as SAS, SQL, NOSQL, graph models. Perform thorough validation of the machine learning output to ensure a high-quality work product.

Requirements

  • Minimum of 5 years’ experience with relevant master’s degree in data science, statistics, Informatics, operations research, computer science, engineering or information systems or a related discipline
  • Data science and visualization i.e., Building data ecosystem and pipelines, Data Processing, Data Wrangling and Analytics (Reports, BI dashboards, ML models)
  • SQL and Python or R programming knowledge
  • Proven ability to translate and influence business requirements against existing analytics and data integration capabilities whilst identifying capabilities challenges and necessary solutions to ensure realistic goal setting for future analytics modelling
  • A proven record in shaping and delivering key programs that optimized performance and delivered project support in matrix environments
  • Experience in advanced analytics processes including gaining insight from structured and unstructured data, predictive modelling and optimization and associated systems/database infrastructure
  • Strong working knowledge of traditional statistical methodologies
  • Strong working knowledge of machine learning methodologies, both supervised and unsupervised learning methods
  • High proficiency in either Python or R
  • Familiarity of best practices in machine learning model development, validation and deployment
  • Solid understanding of life cycle management of the code and code repositories
  • Extensive experience with data wrangling, data preprocessing and data engineering techniques on large datasets
  • Technical expertise, preferably in pharmaceutical clinical or a regulated industry operation, with extensive experience gained through working with diverse and complex data integration, warehousing and ingestion

Responsibilities

  • Lead and execute clinical and operation advanced analytics or system improvements
  • Develop novel data science methodologies to generate deep insights from disparate data sources
  • Provide subject matter expertise to teams in identification and application of the right data science methodology for a business problem
  • Formulate the right business problem in collaboration with business and create necessary analysis plans
  • Develop machine learning methodologies both supervised and unsupervised as appropriate to the business problem
  • Develop scalable and robust code in relevant language such as R or Python in support of data science projects
  • Perform exploratory data analysis, system design analysis, integration design analysis, database design analysis to generate insights and help answer business questions
  • Ensure the life cycle management of the models is maintained through code repositories and version control tools
  • Perform data engineering, data preprocessing and data wrangling activities utilizing different approaches such as SAS, SQL, NOSQL, graph models
  • Perform thorough validation of the machine learning output to ensure a high-quality work product
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