Responsibilities

Influence machine learning strategy for a program/project; explores design options to assess efficiency and impact, develop approaches to improve robustness and rigour

• Be a key contributor to the planning and direction of a project and effectively prioritize goals
• Lead discussions at peer review and uses quantitative skills to positively influence decision making

• Effectively explain technical concepts at all levels in the organization, including senior managers/stakeholders
• Lead, or makes major contributions to, improvements in methodology or initiatives to address capability gaps or increase efficiency
• Represent GSK externally to advance technical capability across the Industry
• Identify opportunities to apply the latest advancements in Machine Learning and Artificial Intelligence to the fields of biology, chemistry, and medicine
• Create algorithms to extract information from large, multiparametric data sets

• Deploy your algorithms to production to identify actionable insights from large databases
• Compare results from various methodologies and recommend best techniques to stake holders
• Design, develop and implement analytical solutions using a variety of commercial and open source tools (common tools include Python, R, TensorFlow)
• Develop and embed automated processes for predictive model validation, deployment, and implementation
• Connect and collaborate with subject matter experts in R&D, GIO-Q, Commercial and Medical

• Make impactful contributions to internal discussions on emerging machine learning methodologies
• Educate the organization both from IT and the business perspectives on these new approaches, such as testing hypotheses and statistical validation of results
• Provide thought leadership: facilitate cross-geography fertilization of ideas and implements key principles/best-practices and guidelines across the categories
• Enable the future BI / Analytics infrastructure and self-service model