Machine Learning Engineer (London)
As part of McKinsey & Company Inc. UK, QuantumBlack helps companies use data to drive decisions. We combine business experience, expertise in large-scale data analysis and visualization, and advanced software engineering know-how to deliver results. From aerospace to finance to Formula One, we help companies prototype, develop, and deploy bespoke data science and data visualisation solutions to make better decisions.
As a Machine Learning Engineer, you are a keen problem solver who uses technology to solve complex analytical problems. You have a deep interest in Big Data technologies, Analytics and Data Science. You know how to engineer beautiful code in Java or/and Scala and/or Python (and Spark) but also can read R and take pride in what you produce.
Who You’ll work with:
You will have the opportunity to work on complex problems with our clients across a number of domains. You will work part of a highly collaborative and cross-functional team of Data Scientists, Data Architects, Engineers and Designers.
What you'll do
- Work closely with Data Scientists and Data Engineers to productionise and deploy machine learning models
- Work with the guild leadership to set the standards for software engineering practices within the machine learning engineering team and support across other disciplines
- Play an active role in leading team meetings and workshops with clients
- Choose and use the right analytical libraries, programming languages, and frameworks for each task
- Produce high-quality code that allows us to put solutions into production
- Refactor code into reusable libraries, APIs, and tools
- Help us to shape the next generation of our products
- Msc or Post-graduate degree educated in computer science or a relevant subject
- Professional experience with object-orientated programming languages such as Scala or C++ or Java
- Good software engineering principals
- Knowledge of Big Data technologies, such as Spark, Hadoop/MapReduce is desirable but not essential
- Strong coding skills in Python.
- Deep knowledge of testing frameworks and libraries
- Good knowledge of database management languages e.g. SQL, PostgreSQL
- Professional knowledge of machine learning environments, such as Regression or Decision Trees or Random Forest or Deep Learning