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What makes a Good Machine Learning Engineer – Qualities & Skills

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30th Aug, 2019
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What makes a Good Machine Learning Engineer – Qualities & Skills

The inclusion of Machine Learning (ML) in mainstream technological applications has made this branch of Data Science, one of the hottest career options right now. As the interest in ML is increasing by the day, it is giving rise to a growing number of job opportunities in the field, with Machine Learning Engineer being one of the most promising jobs

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However, since Machine Learning is still an emerging field, the real challenge lies in finding the right talent for ML jobs. The only problem – there aren’t enough talented and skilled professionals to fill these vacancies. This is where Machine Learning courses come in handy. By enrolling in programs that are specially designed for ML, you will not only learn about ML and the related concepts but also nurture industry skills simultaneously.

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When companies hire Machine Learning Engineers, they look for certain qualities and skills that make an excellent ML Engineer.

And guess what? That’s our topic of discussion today!

What qualities make up a good Machine Learning Engineer?

Before we get into a detailed discussion about the skills and qualities of an ML Engineer, you must first understand the job role. The job of an ML Engineer is neither purely academic-based nor purely research-oriented – it’s a mix of both. Also, while the best ML Engineers need not have a research or academic background, they must have both Software Engineering background and Data Science Experience.

Now, let’s discuss the qualities of a skilled ML Engineer.

1. A strong propensity for programming.

A Machine Learning Engineer is an expert programmer. ML Engineers usually have a Computer Science/Software Engineering background. Hence, they possess an in-depth understanding of Computer Science concepts like Data Structures, Computer Architectures, Algorithms, Computability & Complexity, among other things. Needless to say, ML Engineers have a flair in at least two programming languages and have coding knowledge at their fingertips.

2. A strong foundation in Mathematics and Statistics.

ML Engineers must be well-versed with Mathematical and Statistical concepts including Linear Algebra, Multivariate Calculus, Mean, Median, Variance, Derivatives, Integrals, Standard Deviations, Distributions, etc. Apart from this, they must also know the basic concepts of probability like Bayes rule, Gaussian Mixture Models, Markov Decision Processes, Hidden Markov Models, etc. 

Mathematics, Statistics, and Probability lie at the heart of many ML algorithms, and hence, it is crucial to have a strong foundation in these.

3. An intuitive and creative bent of mind.

While there is no shortage of Software Engineering/CS graduates, there is definitely a shortage of individuals who are driven by curiosity and the will to learn. A good ML Engineer is an intuitive and creative professional. Only then can they use their Mathematical, Statistical, and Analytical skills to find solutions to complex real-world problems. The goal is to develop innovative ways to look at a problem and create numerous possibilities around it. 

4. The innate ability to understand data and derive insights from it.

The ability to understand data and derive valuable insights from it is integral to developing ML algorithms and applications. An ML Engineer must be able to decode and unravel the hidden patterns within raw data, analyze it, and interpret it to find actionable business solutions.

5. A keen sense of business knowledge.

To develop successful ML applications and projects that actually address different business issues, one must know the business domain inside-out. Every business has unique needs and hence, having a keen sense of knowledge about the business domain is essential to develop specific ML applications and projects best-suited for it. Also, customer satisfaction is a pivotal aspect of a business. Hence, a good ML Engineer will always develop models/applications, keeping in mind the unique needs of the customers or the clientele. 

6. The ability to deliver on time. 

When you have a highly demanding and versatile job role as that of an ML Engineer, proper time-management is crucial. An ML Engineer has a lot to do within a stipulated time – analyze and interpret data; build ML models; use the right ML algorithms to train models; perform A/B testing, and so much more. Getting so much done within the allotted time and successfully delivering the project to the clients is a highly appreciated quality.

7. The ability to communicate clearly.

ML Engineers often work with Data Scientists, Data Analysts, and other technical staff. To work in a team, one must possess excellent communication (both written and verbal) skills. Not everyone can communicate or present their ideas clearly for the teammates to see. ML Engineers must bear this quality to be able to explain their findings and models for a clearer understanding of other team members. 

8. A strong passion and drive for work.

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An employee who is driven by a strong passion for the work he/she does is truly a valuable asset for a company. This is a defining quality that sets them apart from a pool of qualified candidates. Recruiters often look for candidates who bear an immense passion for AI and ML and are ever-ready to seek answers. 

These are some of the most valued and demanded quality traits of a Machine Learning Engineer. If you have the right educational background and possess the qualities we’ve mentioned above, you are golden – take our word for it! 

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Prashant Kathuria is currently working as a Senior Data Scientist at upGrad. He describes himself as a data freak and others working with him will agree. Working in Data since more than 3 years in Product companies has taught him that data of today is gold of tomorrow. You will find him brainstoring about new things, or reading about upcoming technologies in his free time.
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