Data Scientist - Clinical & Operational Analytics

Overview

Lead and execute clinical and operational advanced analytics and system improvements, applying data science methodologies to generate insights from disparate data sources.

What you'll do

• 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 supervised and unsupervised machine learning methodologies as appropriate to the business problem.
• Develop scalable and robust code in relevant language such as R or Python or equivalent 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 approaches such as SAS, SQL, NOSQL, graph models.
• Perform thorough validation of the machine learning output to ensure a high-quality work product.

What you'll need

• Minimum of 5 years' experience with a relevant master's degree in data science, statistics, informatics, operations research, computer science, engineering or information systems or a related discipline, OR a minimum of 2 years' experience with a Ph.D. degree in data science, statistics, informatics, operations research, computer science, engineering or information systems.
• 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.
• 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 supervised and unsupervised machine learning methods.
• High proficiency in Python or R or equivalent.
• Familiarity with best practices in machine learning model development, validation and deployment.
• Solid understanding of life cycle management of 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 working with diverse and complex data integration, warehousing and ingestion.
• Excellent communication, interpersonal, and written skills.
• Proficient in English.

Nice to have

• Any data science certifications or relevant education.