- 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 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 different approaches such as SAS, SQL, NOSQL, graph models. Perform thorough validation of the machine learning output to ensure a high-quality work product.
- Individual should have a 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 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 is required.
- 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 is required.
- 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 one of the programming languages either Python or R or equivalent
- 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
- Excellent communication, interpersonal, and written skills
- Analytical Ability /Quality Results Oriented
- Customer Focus
- Global Business Mindset
- Innovation & Transformation
- Solutions Oriented
- Language requirements – Proficient in English
- Certifications – Preferred any data science certifications or relevant education
IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences and healthcare industries. We create intelligent connections to accelerate the development and commercialization of innovative medical treatments to help improve patient outcomes and population health worldwide. Learn more at https://jobs.iqvia.com
IQVIA is committed to integrity in our hiring process and maintains a zero tolerance policy for candidate fraud. All information and credentials submitted in your application must be truthful and complete. Any false statements, misrepresentations, or material omissions during the recruitment process will result in immediate disqualification of your application, or termination of employment if discovered later, in accordance with applicable law. We appreciate your honesty and professionalism.
