Editor’s note: This is the fifth article in a weekly series looking at the top jobs in data science and analytics. The first four pieces looked at
Job title: Machine learning engineer
Reports to: Given the similarity in responsibility to both a software engineer and the data scientist, a machine learning engineer will typically report into very similar people within an organization. In most cases, a lead or principal data scientist or engineer would normally head up the team, and in smaller organizations, a machine learning engineer could report into a head or director of engineering, a chief data scientist or perhaps, in the case of some start-ups, a chief technology officer.
Demand for this role: In most cases a machine learning engineer will partner with a data scientist so that their work will be synonymous with one another; therefore demand for these candidates arguably is extremely similar. While the data scientist will be expected to be stronger in statistics and analytics, the ML engineer will be expert in computer science, generally much stronger at coding. Data scientists will have the ability to build machine learning models, while the engineer will be much more specialist in this field, and for companies serious about cutting edge data science functions, the engineer is key.
That said, the term machine learning engineer is often interchanged with the data scientist, however the focus of the role is very different and the way in which you are deployed within a business will also differ.
Top industries hiring for this job: As mentioned, most businesses that are serious about a high-end data science function within their business will look to build top machine learning models, and it is the machine learning engineer that will provide that. As with many roles in the big data space, the ML engineer is not industry specific, and any business utilizing data science could and will hire ML engineers. Any companies -- from investment banks to tech start-ups, biotech’s to media giants and everything in between -- will benefit from employing talented machine learning engineers, and the skills are extremely transferrable form industry to industry.
Responsibilities with this job: The machine learning engineer will provide the engineering speciality to machine learning and advanced analytics. Their responsibilities, background required and technologies used are very closely related to that of a data scientist, however where the data scientist would lean toward statistics and analytics, the ML engineer is widely focused on data and engineering. The terms can often be interchangeable as mentioned, however there are distinct differences in the profile.
Required background for this job: As with the data scientist, machine learning engineering vacancies will often advertise for a PhD in computer science, physics, software engineering and the like. That said, in the current market many businesses are advertising only for Master’s backgrounds from an academic standpoint. A degree in statistics or mathematics, whilst impressive, wouldn’t necessarily be the most relevant studies for a career in machine learning engineering.
Skills requires for this job (technical, business and personal): On a technical level, the typical skills employed by an ML engineer will be very similar if not identical to the data scientist. For the building of the models, Python or C++ remain the most attractive and most common, while Java can also be preferred by some employers. Softer skills such as a passion for computer science and engineering will also be a plus, and the ability to work well in a collaborative team environment is always a bonus given the nature of the role and close relationship to data scientists.
Compensation potential for this job: A fresh graduate with a PhD or Master’s in computer science would realistically earn an entry salary of around $110,000 to $120,000 in the New York market, increasing to around $150,000 to $180,000 to a candidate with around 5-8 years’ experience. Salaries of $200,000 plus are not uncommon for ML engineers with over 10 years of commercial experience.
Success in this role defined by: Success in this role is certainly defined by possessing the ability to build cutting edge machine learning platforms to aid different businesses in different ways. For example in an investment bank the ML engineer could be expected to build ML models to aid investment decisions, while an engineer in the biotechnology space would be considered successful in aiding disease prediction, which again would differ from an ML engineer building models to predict the success of a marketing campaign for an AdTech business.
Advancement opportunities for this job: Advancement for ML engineers can be similar to both a software engineer of the data scientist. They may move into principal engineers/data scientists to head teams up, eventually heading up engineering or data science groups at director, head or VP level. Position’s as CTO are also not uncommon for machine learning engineers later on in their careers.
