Data Science Specialist

5 days ago

Sydney, New South Wales, Australia beBeeMachineLearning Full time $120,000 - $150,000
Job Opportunity

We seek an experienced Machine Learning Professional to deliver high-quality data science and ML outcomes to clients.

  • Selecting the best technology for a given data science use case
  • Choosing the optimal operational architecture for a ML application
  • Developing robust ML applications
  • Applying MLOps principles to data science workflows, e.g. building reproducible training pipelines
  • Deploying ML workloads on cloud services
  • Automating repeatable routines in ML workflows
  • Providing consulting services that combine architectural vision with hands-on technical expertise using some of the latest AI/ML tools and methods
  • Supporting pre-sales activities including conversations, presentations and writing proposals
  • Contributing to thought leadership activities, including blog and brown-bag content, for the wider AI/ML community
Requirements
  • Commercial software development experience across different industries and organizations
  • Production experience in at least one cloud stack
  • Consulting experience, either with external clients or internally within an organization
  • Fundamental modern software engineering skills (designing and implementing a well-structured code base and software development cycle)
  • Solid programming skills in at least one applicable language, such as Python
  • Cloud engineering skills to architect, use and deploy cloud services in at least one cloud stack
  • An understanding of key machine learning and data science concepts
  • A good grasp of concepts in machine learning development cycle & MLOps, such as model development, model deployment, data versioning etc.
  • Evidence of ML project delivery or self-learning that can be demonstrated
  • Exposure to one or more ML technologies and/or frameworks, with demonstrable production experience
  • The ability to search and evaluate the best tool for a range of different technical problems

We value diverse skill sets and do not necessarily require that you're skilled with all of the above. If you feel you don't fit all the experience/skills listed above we still encourage you to apply.

Key responsibilities:

  • Technology selection: Selecting the best technology for a given data science use case
  • Operational architecture: Choosing the optimal operational architecture for a ML application
  • ML application development: Developing robust ML applications
  • MLOps: Applying MLOps principles to data science workflows, e.g. building reproducible training pipelines
  • Cloud services: Deploying ML workloads on cloud services
  • Workflow automation: Automating repeatable routines in ML workflows
  • Consulting services: Providing consulting services that combine architectural vision with hands-on technical expertise using some of the latest AI/ML tools and methods
  • Pre-sales activities: Supporting pre-sales activities including conversations, presentations and writing proposals
  • Thought leadership: Contributing to thought leadership activities, including blog and brown-bag content, for the wider AI/ML community

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