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ML Ops Engineer - Data Science & Machine Learning (3253052489)

Technology 信息技術

IT Auditor


GCSE/Scottish Standard Grades A-Level Postgraduate or above 碩士或以上 Undergraduate or above 學士或以上 Intermediate apprenticeship 學徒

PSW Apprenticeship 學徒 華語工作 Contract 合同制 Full-Time 全職 Internship 實習 Part-Time 兼職


Lasswade, Midlothian, United Kingdom

End Date Friday 24 June 2022 Salary Range 52,912 - 85,982 We support agile working – click here for more information on agile working options. Agile Working Options Flexible / Variable Hours, Other Agile Working Arrangements / Open to Discussion Job Description Summary A machine learning (ML) data engineer is required to join the Data Science & Machine Learning team in the Group Chief Information office. This role will be to provide data expertise and hands-on engineering development to support data sourcing, feature engineering and ML pipeline automation to support the development and implementation of machine learning models addressing technology and operational resilience at enterprise level. Job Description Lloyds Banking Group is the UK’s biggest Retail, Digital and Mobile bank with over 25 million customers. Our products touch the lives of millions, so we have a big responsibility. Being data led is at the heart of our strategy to help Britain prosper, backed with significant investment into areas such as Data Science, Machine Learning (ML) and Engineering. And it is the role of our Chief Information Office (CIO) to provide the backbone and infrastructure upon which the bank of the future sits. CIO Technology Resilience & Workplace Engineering (TRWE) is the major hub for resilience and technology advancement within the Group. So why join CIO as a Machine Learning Data Engineer? You will be part of a growing team of Data Scientists and Data Engineers in a highly experienced team that solves technology and operational resilience problems for the whole enterprise. So that means helping us craft and implement machine learning models that consume and analyse real time event log data. These models will address everything from cyber security threat hunting to predictive maintenance of systems across public cloud, private cloud and on premise systems. You’ll also help CIO play a pivotal role in designing new ML capabilities for the Group. This will see you play a major part in design and engineering of the ML assets that support new technologies and helping to craft the Group’s strategies. This is the foundation of monitoring and alerting which is needed as we improve cyber security, technology resilience and our DevOps ways of working. We want you to employ machine learning and data science engineering techniques in a huge way to rapidly transform how we deliver key projects, optimise new cloud platforms and other various strategic transformations. Interested? Here are some real examples of the work you could be doing: Responding to enterprise project requests for ML Data engineering expertise through wrangling data from various sources (system and colleague activity log data stored across cloud and on-premise platforms). Designing and building automated data processing solutions and implementing into dedicated MLOps pipelines that ensure our infrastructure is secure, resilient, efficient, and fast. Supporting the creation of the monitoring, detection and alerting models and capability critical to security improvement - including sophisticated event pattern classification to identify potential cyber threats in data streams. Developing new anomaly detection pipelines that analyse user and systems behaviour and help us detect potentially damaging or malicious activity. Building capability to predict when and what incidents are likely to happen so that we address root causes before security or infrastructure is impacted. Collaborating with many other engineering, security and technology teams across the organisation. Being a hands-on researcher, designer and developer of industry leading ML Data techniques. What about you? We're keen to speak to you if you're an expert in Data Engineer with validated machine learning operations (MLOps) practice in a commercial or research setting. You should have a keen curiosity about automation approaches and ML techniques, especially those used for taking on very large data sources and data streams. Interaction with the technical and engineering community both internally and externally (other companies, software vendors and universities for example) plays a key part in this role so you should be practised in these kinds of interactions. You’ll also need to possess a developed communication kit bag – adept at conversing to technical/non-technical audiences and influencing widely. The following skills are key to the role: Expert engineer with experience across languages such as Python, Scala, Java and/or SQL. You have built large-scale data pipelines and data centric applications. Experience across data engineering tools and platforms (Kafka, Spark, Flink, Storm, Hadoop etc) on high volumes of heterogeneous data. Implementing machine learning into cloud platforms with specific focus on Azure ML Services, Databricks and Google Vertex AI. Knowledgeable about data modelling and data management techniques and can speak to their various trade-offs. Hands-on experience of feature engineering and pipeline orchestration as applied to sophisticated modelling techniques such as density based clustering techniques, support vector machines, auto-encoders, multi-class clustering models and generative adversarial networks. Experience in cyber-security and predictive maintenance is helpful. Ability to work in an agile environment, interacting with scrum masters and authoritatively utilising tools such as Jira, GitHub, GitHub Project and GIT. A solid grasp of data engineering for ML. Familiarity with automation and orchestration, 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. We want you to help us become an outstanding machine learning team Qualifications: You must have a Masters or PhD in mathematics, applied science, computer science or related quantitative field Academic qualification must be complimented with hands on experience of data engineering for ML in a commercial or academic environment And how will we help you super charge your career? We will ensure that you'll get exposure to a host of techniques, technologies and people to broaden your ML and engineering horizons. We can also give you the stretch you desire in terms of ML Data Engineering with opportunities to widen your skills in engineering data-led solutions using sophisticated technology stacks. Training from a wide variety of resources and big name providers is available to help you support our projects and advance your own personal interests Offering you both well-funded projects and a high profile - we'll provide you with a diverse, energising and informal environment that focuses on equal opportunity and real career progression. If you like the sound of this role then we’d love to hear from you Together we make it possible. At Lloyds Banking Group, we're driven by a clear purpose; to help Britain recover. Across the Group, our colleagues are focused on making a difference to customers, businesses and communities. With us you'll have a key role to play in shaping the financial services of the future, whilst the scale and reach of our Group means you'll have many opportunities to learn, grow and develop. We're focused on creating a values-led culture and are committed to building a workforce which reflects the diversity of the customers and communities we serve. Together we’re building a truly inclusive workplace where all of our colleagues have the opportunity to make a real difference. Summary Location: Edinburgh; Wythenshaw; Bristol; London Type: Full time