About the Role
As a Lead Data Science Engineer, you will be designing, developing and implementing data science & machine learning solutions to tackle large and small technology problems across the Group. Your role involves hands-on development through the entire data science life cycle, from academic and industry research to crafting operational model pipelines. This will be complemented by leading a small, highly skilled team with whom you will collaborate, teach, learn and ensure they are an industry leading example of what data science can achieve.
You'll be part of a growing team of Data Science Engineers in a highly influential role with considerable executive level focus as the Group matures its software engineering and data led approach. You will engage directly with customers, prioritise multiple requests, and educate business areas on how to use data throughout the decision-making process. You'll employ machine learning and data science techniques to transform areas from delivering key engineering projects to optimising new cloud platforms and enhancing group security.
Responsibilities
- Responding to enterprise project requests for data science and machine learning expertise.
- Wrangling data from various sources (system and colleague activity log data stored across cloud and on-premise platforms).
- Researching and developing predictive models using approaches new to the organisation and implementing into dedicated MLOps pipelines that ensure infrastructure choices are secure, resilient, efficient and fast.
- Developing new analytics and models that help us truly understand our colleague experience to improve the engineering choices we make.
- Building capability to predict when and what incidents are likely to happen so that root causes are addressed before security or infrastructure is impacted.
- Being a hands-on researcher, designer and developer of industry leading techniques.
Requirements
Experience
- Proven practice as an expert and established Data Scientist or Machine Learning Engineer for a minimum of 5 years.
- Leadership experience to lead, develop and support a multi-site technical team.
- Experience interacting with the technical and engineering community both internally and externally.
- Developed communication skills, adept at conversing with technical/non-technical audiences and influencing widely.
- Code using Python, SQL or similar languages and familiarity with modern data technologies such as Apache Spark, Hadoop and model workflow tools like MLFlow.
- Experience of developing and implementing data science or machine learning into Cloud platforms with specific exposure to Azure Databricks and Google Vertex AI being useful.
- Hands-on experience of feature engineering, development, validation and implementation as applied to sophisticated data science and modelling techniques (such as density-based clustering techniques, support vector machines, auto-encoders, multi-class clustering models and convolutional neural networks).
- Experience of applying machine learning to cyber-security or other domains using monitoring and alerting in data streams is advantageous.
- Ability to work in an agile environment, interacting with scrum masters and authoritatively utilising tools such as Jira, GitHub, GitHub Project and GIT.
- A validated understanding of data engineering for data science and machine learning. Familiarity with graph/NoSQL databases, relational data bases, data streaming and how these can be used to optimise model performance.
- An active curiosity about the ongoing development of data science techniques, cloud capability and technology.
Qualifications
- Masters or PhD in mathematics, applied science, computer science or related quantitative field.
- Demonstrable years' experience within data science and/or machine learning in a commercial, not-for-profit or research setting.