ML Engineer for Life Sciences

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

Syngenta is one of the world’s leading agriculture innovation company (Part of Syngenta Group) dedicated to improving global food security by enabling millions of farmers to make better use of available resources. Through world class science and innovative crop solutions, our 60,000 people in over 100 countries are working to transform how crops are grown. We are committed to rescuing land from degradation, enhancing biodiversity and revitalizing rural communities.

A diverse workforce and an inclusive workplace environment are enablers of our ambition to be the most collaborative and trusted team in agriculture. Our employees reflect the diversity of our customers, the markets where we operate and the communities which we serve. No matter what your position, you will have a vital role in safely feeding the world and taking care of our planet.

To learn more visit: www.syngenta.com

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, toxicology, environmental science) is a plus
• Experience working in an academic or industrial R&D setting
• Strong Python skills and familiarity with standard data science tooling for data-driven modelling / machine learning
• Understanding of the model lifecycle and tools to manage it, as well as technical aspects such as deployment, containerization/virtualization, and handling metadata. Experience with DataIKU/DSS and AWS Bedrock is a plus, but not essential
• Strong analytical thinking and problem-solving skills, adaptability to different business challenges and openness to new solutions and different ways of working
• Curiosity and ability to acquire domain knowledge in adjacent scientific areas to effectively work across internal teams and quickly get up to speed with different modelling approaches
• Understanding of mathematical/mechanistic modelling is a plus
• Solid understanding of Web APIs and how they can be used to operationalize models
• Adaptable to different business challenges and data types / sources
• Able to learn and utilize a range of different analytical tools and methodologies – not fixed in a particular methodology
• Strong collaborative, networking and relationship building skills
• Uses visualization and storytelling with data to communicate results to parties with varying levels of technical proficiency
• Enjoys working a highly diverse working environment comprising multiple scientific disciplines, nationalities, and cultural backgrounds
• Able to manage own time and deals effectively with conflicting workloads, in agreement with key customers.

Note: Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity, marital or veteran status, disability, or any other legally protected status. 

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