提升你的 Machine Learning 工程师职业生涯

在开始学习高级机器学习之前，了解可扩展数据科学和数学（包括线性代数和多元微积分）的基础知识是很有帮助的。 还建议学习编程，特别是 Python，以及 SQL 的基本知识。 许多学习者希望在学习高级课程之前，先从机器学习的基础知识开始。 ‎

With your master’s degree in data science, you can explore many data science jobs or continue your educational pursuits by applying for a PhD in data science. The roles below illustrate some of the jobs you can pursue once you’ve earned your master’s degree in data science, includng machine learning engineer:

• Senior data scientist: Working as a data scientist typically requires a bachelor’s degree, but you may qualify for more senior-level roles with a master’s. Data scientists typically design algorithms to collect and interpret data.
• Data engineer: A data engineer designs and builds systems to handle a lot of data so that data scientists and data analysts can work with it.
• Data architect: A data architect drafts frameworks businesses can use to handle data, often with the goal of making sure it meets any compliance requirements.
• Machine learning engineer: Machine learning engineers often sit on data science teams. They design, build, and maintain machine learning algorithms and systems.
• Statistician: Statisticians can work for public or private organizations and often look for trends in data by collecting and interpreting it.
• SQL developer: This role sits in between software development and database engineering. SQL developers often work to create on maintain SQL-specific databases.

Read more: Is a Master’s Degree in Data Science Worth It?‎

随着越来越多的公司力求运用这些技术和人工智能 (AI) 来自动执行更多的流程，机器学习技能可以打开广阔就业前景的大门。一些公司可能会专门聘请机器学习工程师，但是机器学习技能对于数据科学家、数据分析师和数据工程师也可能很重要。

机器学习专家也可以担任更专业的角色。金融业中的许多公司可能会聘用商业智能分析师和决策科学家，他们可以运用机器学习技能让系统自动提供市场见解。如果公司要建立依赖语音识别或其他人工输入的物联网 (IoT)，则可能会聘用自然语言处理工程师或以人为本的机器学习设计师。 ‎