Data Science AI Ops Lead

Job Description

The successful candidate will be part of building a new, close-knit team of deeply technical experts and together have the chance to create tools that will advance the standard of healthcare, improving the lives of millions of patients across the globe. This platform will support major AI initiatives such as clinical trial data analysis, knowledge graphs, imaging & omics for our therapy areas. You will also have responsibility to help provide the frameworks for data scientists to develop scalable machine learning and predictive models with our growing data science community, in a safe and robust manner.As a strong software leader and an expert in building complex systems, you will be responsible for inventing how we use technology, machine learning, and data to enable the productivity of AstraZeneca. You will help envision, build, deploy and develop our next generation of data engines and tools at scale. You will be bridging the gap between science and engineering and functioning with deep expertise in both worlds.Key Accountabilities Own the development roadmap to build and operationalise our data science environment, platforms and tooling. Support any external opportunities, through close partnership and engagement such as Benevolent.AI collaboration. Deployment of systems, applications and tooling for data science on cloud environments. Understanding of the necessary guardrails required for different use cases and data sensitivities. Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU). Provide the necessary infrastructure and platform to support the deployment and monitoring of ML solutions in production Optimizing solutions for performance and scalability. Liaise with the Data Engineering team to ensure that the platform and the solutions deployment therein benefit from an optimised and scalable data flow between source systems and analytical models Implementing custom machine learning code and developing benchmarking capabilities to monitor drift of any analyses over time. Understanding of the latest AI webservices and data science tools, from DataBricks to citizen data science tools like Dataiku, C3.AI and Domino. Experience working on regulatory data would be helpful but not essential. Liaise with other teams to enhance our technological stack, to enable the adoption of the latest advances in Data Processing and AI Being an active member of the Data Science team, you will benefit from, and contribute to, our expanding bank of Data Science algorithms and work efficiently with our data science infrastructure. Appreciation of how to optimise predictive models, run in production and monitor. Experience running a service team will be beneficial. Testing and assessing the quality of new tools. Line management responsibilities as well as team recruitment, training provision and coachingCandidate Knowledge, Skills and ExperienceBSc in Computer Science or related quantitative field or MSc/Ph.D degree in Computer Science or related quantitative field. More than 2 years of experience and demonstrable deep technical skills in one or more of the following areas: machine learning, recommendation systems, pattern recognition, natural language processing or computer vision. Experience managing an enterprise platform and service, handling new customer demand and feature requests. Strong software coding skills, with proficiency in Python and Scala preferred. Significant experience with AWS cloud environments, working knowledge of Google and Azure platforms. Knowledge of Kubernetes, S3, EC2, Sagemaker, Athena, RDS and Glue is essential. Certification in appropriate areas will be viewed favourably. Experience with best practice of data transport and storage within cloud system. Experience building large scale data processing pipelines. e. g. Hadoop/Spark and SQL. Experience provisioning computational resources in a variety of environments. Experience with containers and microservice architectures e.g. Kubernetes, Docker and serverless approaches. Experience with automation strategies e.g. CI/CD, gitops. Use of Data Science modelling tools e.g. R, Python, SAS and Data Science notebooks (e.g. Jupyter). Creative, collaborative, & product focused. Ability to just get things done.,

Keyskills :
natural language processinguse casesdata flowweb servicesdata sciencedata analysisdata transportdata processingmedical affairsline managementmodelling toolscomplex systemscomputer sciencemachine learningtechnical skills