Data Scientist

Job Description

Workdays : Mon to Fri Job Description: The Data Scientist is responsible for contributing to the development of the strategy for Analytics, Reporting and Metrics building in line with overall Business Information Services strategy leveraging visualization tools/business intelligence platforms. Your role will include the following:- Coordinate with different functional teams to implement models and monitor outcomes. Develop processes and tools to monitor and analyze model performance and data accuracy. Leveraging business intelligence tools i.e Spotfire for analysis and review. Develop custom data models and algorithms to apply to data sets. Providing specialist support for development, interpretation and application of machine learning models Applying analytical and statistical methods to solve identified use cases in an agile manner using various data sources and analytical tools Work with key stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions. Liaising, advising and negotiating with key customer groups on requirements and proposing innovative solutions to meet their data modelling requirements Responsible for the development and implementation of capabilities to make provisioning of routine requests more routine, structured and efficient Contributing to initiatives that enable faster and more effective data modelling/analysis and provisioning Ensuring systems and code, documentation are inspection ready What Required: Were looking for someone with 3-5 years of experience manipulating data sets and building statistical models, has a Masters or PHD in Statistics, Mathematics, Computer Science. Experience using statistical computer languages Python & SQL (R Considered) to manipulate data and draw insights from large data sets. Experience working with and creating data architectures. Experience of clinical data and domains (Experience working within Clinical Trials or the wider Pharmaceutical industry) preferable but not necessary Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks. Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications. Excellent written and verbal communication skills for coordinating across teams. A drive to learn and master new technologies and techniques.