Data Scientist vs. Machine Learning Engineer | Summary and Q&A

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June 17, 2021
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DeepLearningAI
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Data Scientist vs. Machine Learning Engineer

TL;DR

Gain insights from industry experts on the differences between data scientists and machine learning engineers in the tech industry.

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Key Insights

  • 🧑‍🔬 The roles of data scientists and machine learning engineers have evolved and continue to change based on industry needs and innovations.
  • 🏑 The blurriness in defining these roles is due to the evolving nature of the field and the different requirements across companies and industries.
  • 😮 There is a need for specialized skills in both data science and machine learning engineering, but there is also a rising trend of automation and citizen data scientists.
  • 😒 The use of data science and machine learning spans across various industries, with a particular emphasis on healthcare, finance, and climate change.
  • 🍧 Having a PhD or academic experience can be advantageous, but practical experience, domain expertise, and problem-solving skills are also valuable in securing industry jobs.

Transcript

hi everyone welcome my name is sandia simhan i'm the director of marketing at deeplearning.ai welcome to our expert panel data scientist versus machine learning engineer this event is presented to you by deep learning dot ai and forth brain fort brain is a company that creates accessible and flexible pathways to ai careers by training candidates wi... Read More

Questions & Answers

Q: What is the difference between a data scientist and a machine learning engineer?

Data scientists apply the scientific method to data, conduct exploratory data analysis, and collaborate with the business. Machine learning engineers take the models built by data scientists and make them work in production environments at scale.

Q: Do you need a PhD or academic experience to work as a data scientist or machine learning engineer?

While a PhD or academic experience can be advantageous, it is not necessary. Practical experience, project work, and domain expertise can also be valuable in securing industry jobs.

Q: How can someone gain practical experience in the data science or machine learning field without prior professional experience?

Participating in internships, capstone projects, or hands-on project work in relevant areas can provide valuable practical experience. Building projects and focusing on problem-solving skills can also help develop real-world skills.

Q: Is it possible to be both a data scientist and a machine learning engineer?

While the roles of data scientists and machine learning engineers have overlapping skills, they are distinct. However, in some companies or situations, a data scientist may be responsible for both roles, especially in smaller organizations.

Summary & Key Takeaways

  • Data scientists require a strong grasp of coding, math, statistics, and subject matter expertise in their respective industries. They focus on building and optimizing machine learning models.

  • Machine learning engineers focus on coding, math, and statistics but do not require deep subject matter expertise. Their role centers around scaling and productionizing models for organizational workflows.

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