Inchcape is one of the world’s largest independent automotive distributors and retailers. Our core purpose is to manage the end-to-end distribution logistics and customer experience for the best car brands in the world. We work on behalf of brands including BMW, Mercedes, Jaguar-Land Rover; VW Group (VW, Audi and Porsche); Toyota/Lexus and Subaru. Founded in 1847, we are listed on the London Stock Exchange and have 18,700 employees in 32 markets across Asia, the Pacific, South America, Africa, and Europe. Our core business is distribution in emerging markets, whereby we are exclusively responsible for an auto manufacturer’s sales and brand from the factory gate to the end sale in a specific country. This includes logistics, advertising & marketing, customer experience and retail. **Job Role & Responsibilities**: You will provide mentorship to a global cross-functional team comprising of various functions like data science, statistical analysis, automation, BI, analytics architecture, experimentation, and business analysis. You will work with a team of MLOps Engineers & Architects ensuring they perform well, and projects are delivered within time & budget. You will also provide technical oversight and guidance for our acceleration partners who we’ve employed to help push forward our business objectives. At your core, you are passionate about building and deploying analytics that unlock true, measurable incremental revenue for the business - simply producing decks and reports is not enough for you! A career with Inchcape provides the opportunity to lead a team in a multinational business where data analytics is being put at the heart of the business strategy. You will experience in an exciting and rapidly growing team and an opportunity to be part of a truly global company. **What you’ll do** **Work a growing team of MLOps Engineers** - As a Senior ML Ops Engineer you will help to develop an infrastructure for Machine Learning models to be deployed and you have expertise in Python for programming. - You will also be the person who productionises the code developed by Data Scientists & Analysts. **Build the ML Infrastructure using a hybrid cloud of Azure & GCP** - A large part of the role will involve develop the infrastructure behind the ML models and this will be based exclusively on Azure and some parts on GCP. - You must have extensive experience using services such as ML flow, Apache beam, Apache Kafka - If you have experience deploying ML models it will be highly advantageous. **Have great knowledge of Software Engineering Fundamentals, CI/CD & Architecture Design** - Have a strong foundation in Software Engineering as you will be helping to bring this knowledge into the team. - This may include TDD but if you have experience with Extreme Programming this is highly valuable as you will be working on the infrastructure side of things. Any exposure to CI/CD is highly advantageous. - You will be doing a mix of both hands on work and architectural design so must be comfortable helping to create processes and frameworks. You will also be involved in some business facing responsible so again any prior experience in stakeholder management is highly valuable. **Who you are**: You will have real-world, full-stack experience of developing and deploying impactful data science projects in production and manage ML Lifecycle after deployment You will have: - 5+ years of machine learning productization experience, with expertise in data and statistical modelling - 2+ years of management of high-performance team - Full stack experience in data collection, aggregation, analysis, visualization, productionisation, and monitoring of ML products - aka MLOps - Ability to develop ML Product with good engineering practice and mindset - Strong desire to solve tough problems with scientific rigour at scale - An understanding of the value derived from getting results early and iterating - Strong skills in Python and machine learning and deep learning libraries (Pytorch and Tensorflow) - Passion to answer Product/Engineering questions with data - Experience with data processing using Spark / Databricks is a plus. - Experience using cloud such as Azure/GCP is a plus. **Relevant Experience** - Proven track record of leading high-performing data teams that successfully implemented advanced quantitative analyses and statistical modelling with measurable impact on business performance. - 5+ years of working experience in MLOps role out of which 2+ years of significant development experience. ***Leadership and communication** - Strong stakeholder engagement skills including C-levels, Product, Engineering, Operation/Strategy Team. - Getting business stakeholders on-side and understanding in the value of advanced analytics. - Solid leadership skills and is proactive within an ambiguous environment. - Creative and strategic thinker, innovative problem-solving skills, highly organized, with the ability to handle multipl