What is the Difference Between Machine Learning Engineer Vs Machine Learning Scientist?
Machine learning scientist is not that much different from machine learning engineer. But there is difference between these two specialists who play a crucial role in developing AI or ML based models for real-life use.
Machine Learning Engineer vs Machine learning Scientist
Machine Learning Engineer actually works in the branch of artificial intelligence who is responsible for creating programmes and algorithms that enable machines to take actions without being directed. Self-driving car is one the best example a machine learning engineer can develop with coding using the relevant algorithms. Their main role is to provide computers with the ability to learn automatically and improve from experience, without being programmed.
While on the other hand, Machine Learning Scientists work in the research and development of algorithms that are used in adaptive systems in AI field. These scientists actually, build methods for predicting product suggestions or recommendations and product demand or forecasting and explore big data to automatically extract patterns for large-scale machine learning and pattern recognition.
Responsibilities of a Machine Learning Scientists:
> Design and implement ML information extraction
> Probabilistic matching algorithms and models
> Research and develop innovative, scalable solutions
> Scale machine learning algorithm that powers our platform
> Will be part of our Data Science & Algorithms team
> Collaborate product management and other team members
> Work closely with ML engineers, data scientists and data engineers
Responsibilities of a Machine Learning Engineers:
> Can understand and use computer science fundamentals
> Ability to use exceptional mathematical skills for computations
> Produce project outcomes and issues need to be fixed
> Collaborate with data engineers to build data and model pipelines
> Manage the infrastructure and data to bring code to production
> Can demonstrate end-to-end understanding of applications
> Able to build algorithms based on statistical modelling procedures
> Can build and maintain scalable machine learning solutions in production
> Capable to use data modelling and evaluation strategy to find patterns
> Can lead on software engineering and software design
> Can research and implement best practices to improve the ML infrastructure
> Communicate and explain complex processes to other common people
> Can analyse large, complex datasets to extract insights
The role of a ML engineer compare to ML scientist much more critical and important to develop a AI or ML based model. However, many roles are common between them depending on the type of AI model they are working and the industry or company looking for the ML expert for their such projects analysis and development. However, both must have understanding of machine learning and its sub-branches.
Hiring ML scientist or ML engineers is a challenging task, especially for companies looking for such specialists for remote locations or only as per their projects needs. Cogito can help you to hire machine learning engineer from the world’s best institutions or working at reputed organizations. Cogito is engaged in machine learning hiring for different types of companies and industries as per their customize needs.
