Machine Learning Engineer (Sydney)
This role will be based in our QuantumBlack hub in Sydney, Melbourne or Perth and you will work as part of McKinsey & Company, joining a highly collaborative team of exceptionally talented Data Scientists, Data Architects, and Engineers.
QuantumBlack help companies use their 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.
Right now, you’re a very strong software engineer with a deep interest in Analytics and Data Science. You know how to engineer beautiful code and want to work on varied data science projects across multiple industries. You’re exceptionally talented, and looking to work in a highly collaborative and cross-functional team consisting of Data Scientists, Data Architects, and Engineers. You are someone who is looking to constantly develop your skills and adapt to new languages, trends and frameworks.
What you'll do
- Work closely with Data Engineers and Data Scientists to create analytical variables, metrics, and models
- Work closely with data scientists to solve difficult engineering and machine learning problems and produce high-quality code
- Choose and use the right analytical libraries, programming languages, and frameworks for each task
- Develop your abilities and understanding of data science methodologies and approaches
- Contribute to best coding and engineering practice across QuantumBlack and our projects
- Refactor code into reusable libraries, APIs, and tools
- Deep and passionate knowledge of software engineering principles, practices
- Proven knowledge of object-oriented programming e.g. Scala, Java, C++ etc.
- Knowledge of at least one scripting language e.g. Python, Perl, R etc.
- Deep knowledge of testing frameworks and libraries
- Good knowledge of database management languages e.g. SQL, PostgreSQL
- Knowledge of statistics, machine learning and data analytics techniques
- Knowledge of Big Data technologies, such as Spark, Hadoop/MapReduce is desirable but not essential
- Commercial application of data science work is desired but not essential