Machine Learning Engineer in Hook

At Elanco (NYSE: ELAN), we are dedicated to innovation and delivering products and services to prevent and treat disease in farm animals and pets. We pride ourselves on fostering a diverse and inclusive work environment, believing that diversity is the driving force behind innovation, creativity, and overall business success.

Driven by the quickening pace of innovation, Animal Health is on the verge of a revolution, powered by digital business models, technology, and data. Elanco IT is a catalyst for change, partnering to identify and deliver transformative solutions to solve our biggest business problems. This includes four strategic priorities:

• Pipeline Acceleration: Optimise the search and approval of high impact medicines with a focus on speed, cost, and precision.
• Manufacturing Excellence: Improve the efficiency, quality, and consistency of core manufacturing processes, specifically execution and equipment effectiveness.
• Sales Effectiveness: Simplify the process to find, trust, and consume relevant customer insights that drive sales growth and improved engagement.
• Productivity: Expand operating margin through efficiency by systematically reducing our operating expenses across the company, improving profitability.

Your role as a Machine Learning (ML) Engineer at Elanco will involve:

• Custom Model Development: Design, build, and train bespoke ML models tailored to specific business needs, from initial prototype to full implementation.
• Third-Party Model Utilisation: Identify, tune, and deploy third-party ML models, covering proprietary and open-source models.
• Production Deployment: Manage the deployment of ML models into our production environments, ensuring they are scalable, reliable, and performant.
• MLOps and Automation: Build and maintain robust MLOps pipelines for CI/CD, model monitoring, and automated retraining.
• Data Pipeline Construction: Collaborate with data engineers/stewards to build and optimise data pipelines that feed ML models, ensuring data quality and efficient processing for both training and inference.
• Cross-Functional Collaboration: Work closely with data scientists, product managers, and software engineers to define requirements, integrate models into applications, and deliver impactful features.
• Code and System Quality: Write clean, maintainable, and well-tested production-grade code. Uphold high software engineering standards across all projects.
• Performance Tuning: Monitor and analyse model performance in production, identifying opportunities for optimization and iteration.

What You Need to Succeed (minimum qualifications):

• Educational Background: A Bachelor’s or Master’s degree in Computer Science, Software Engineering, Artificial Intelligence, or a related quantitative field.
• Programming Excellence: Advanced proficiency in Python and deep experience with core ML/data science libraries (e.g., PyTorch, TensorFlow, scikit-learn, pandas, NumPy).
• Software Engineering Fundamentals: Strong foundation in software engineering principles, including data structures, algorithms, testing, and version control (Git).
• ML Model Deployment: Proven, hands-on experience deploying machine learning models into a production environment.
• MLOps Tooling: Experience with MLOps tools and frameworks and containerisation technologies (Docker, Kubernetes).
• Cloud Platform Proficiency: Practical experience with Public Cloud, specifically Microsoft Azure and Google Cloud Platform (GCP) and their ML services (e.g., Azure ML, Vertex AI).
• DevSecOps: Proven experience with CI/CD, Git SCM, Containerisation (Docker, Kubernetes), Infrastructure-as-Code (HashiCorp Terraform).
• Machine Learning Theory: Solid understanding of the theoretical foundations of machine learning algorithms, including deep learning, NLP, and classical ML.
• Problem-Solving: A pragmatic and results-oriented approach to problem-solving, with the ability to translate ambiguous requirements into concrete technical solutions.
• Industry Experience: A broad understanding of life science, covering the business model, regulatory/compliance requirements, risks, and rewards.
• Communication: Excellent communication skills, capable of articulating complex technical decisions and outcomes to both technical and non-technical stakeholders.

Additional Information:

• Travel: 0-10%
• Location: Hook, UK - Hybrid Work Environment

Don’t meet every single requirement? At Elanco, we are dedicated to building a diverse and inclusive work environment. If you think you might be a good fit for a role but don’t necessarily meet every requirement, we encourage you to apply.