Transform patients’ lives through technology, data, and innovative ways of working, and tackle longstanding life sciences challenges at AstraZeneca's Evinova
Requirements
- Deep understanding of the Data Science Lifecycle (DSLC) and the ability to shepherd data science projects from inception to production within the platform architecture
- Expert in MLflow, SageMaker, Kubeflow or Argo, DVC, Weights and Biases, and other relevant platforms
- Strong software engineering abilities in Python/JavaScript/TypeScript
- Expert in AWS services and containerization technologies like Docker and Kubernetes
- Experience with LLMOps frameworks such as LlamaIndex and LangChain
- Ability to collaborate effectively with engineering, design, product, and science teams
- Proven track record of deploying algorithms and machine learning models into production environments
Responsibilities
- Lead the development and management of MLOps systems for clinical trial design, planning, and operational optimization
- Partner closely with data scientists to shepherd projects from embryonic research stages into production-grade ML/AI capabilities
- Leverage and teach modern tools, libraries, frameworks and standard methodologies to design, validate, deploy and monitor data pipelines and models in production
- Establish systems and protocols for entire model development lifecycle across a diverse set of algorithms, conventional statistical models, ML and AI/GenAI models
- Enhance system scalability, reliability, and performance through effective infrastructure and process management
- Ensure that any prediction we make is backed by deep exploratory data analysis and evidence, interpretable, explainable, safe, and actionable
- Drive precision and systemic cost efficiency, optimized system performance, and risk mitigation with a data-driven strategy, comprehensive analytics, and predictive capabilities
Other
- HS Diploma or GED
- Minimum of 2 years in ML/AI operations engineering roles
- Customer-obsessed and passionate about building products that solve real-world problems
- Highly organized and meticulous, with the ability to manage multiple initiatives and deadlines
- Collaborative and inclusive, fostering a positive team culture where creativity and innovation thrive
