ML Engineer For Life Sciences

Job Description

Role purpose Co-Lead operations of the predictive modelling platform and act as key bridge between R&D IT and R&D Set strategic direction for the modelling platform and guide further technical development Design and develop models to generate new content using machine learning models in a secure, well-tested, and performant way Confidently ship features and improvements with minimal guidance and support from other team members Establish and promote community standards for data-driven modelling, machine learning and model life cycle management Define and improve internal standards for style, maintainability, and best practices for a high-scale machine learning environment. Maintain and advocate for these standards through code review. Support diverse technical modelling communities with governance needs Engage and inspire scientific community as well as R&D IT and promote best practices in modeling Accountabilities Acts as R&D IT co-lead and subject matter expert for the modelling platform, providing strategic direction as well as overseeing technical and scientific governance aspects Works closely with R&D to ensure platform remains fit for purpose for changing scientific needs Engages with modelling communities across R&D to understand applications, recognize opportunities and novel use cases and prioritizes efforts within the platform for maximum impact Develops Python code, scripts and other tooling within the modelling platform to streamline operations and prototype new functionality Provides hands-on support to expert modellers by defining best practices on coding conventions, standards etc. for model deployment and quality control Explores, prototypes and tests new technologies for model building, validation and deployment, e.g. machine learning frameworks, statistical methods, and how they could be integrated into the platform to boost innovation Monitors new developments in the field and maintains awareness of modelling approaches taken by other companies, vendors, and academia. Works with external collaborators in academia and industry to understand and integrate their complementary capabilities Critical knowledge, Experience & Capabilities Background in predictive modelling in the physical or life sciences at a postgraduate level Prior wet-lab experience (e.g. biology, chemistry, toxicolog