There are multiple sources that predict the exponential growth of data in 2020 and beyond. Machine data is increasing at a rapid pace with a growth rate of 50x, and human and machine-generated data is increasing with a growth rate of 10x. The exponential growth of data is a clear indicator of the increasing demand for data-based jobs. Data skills are among the most in-demand skills in the industry today and acquiring these skills can help your chance of career success. With data-based jobs gaining momentum, there are a string of career options available to explore your data skills. Of the many, Data Scientist vs Machine Learning Engineer are the two hottest trending jobs in the industry that go hand in hand. And could be of particular interest for anyone looking to make a career transition. Data scientist vs machine learning engineer are two technological marvels that work on similar principles – data, algorithms, and insights making a case for argument on which career path to choose. For people deliberating over which career to choose, here’s a helping hand to simplify the choices over data scientist vs machine learning engineer- which one is for you?

Data Scientist vs Machine Learning Engineer – What is the difference?

Before we dive in, let’s let the cat out of the bag. There is quite a lot of overlap between data scientists and machine learning engineers, and there is no right or wrong starting point when choosing a career path. The truth is, it does not make a difference whether you choose to start out as a data scientist or a machine learning engineer! Acquiring data skills will make you earn more and also have some of the most in-demand tech skills out there, irrespective of whether they fall under the spectrum of data science or machine learning. Sounds good? Let’s dive in!

What is a Data Scientist?

So, who is a data scientist and how do you exactly frame a definition for a data scientist? Data scientists are a novel breed of superheroes in the analytics industry that are part computer scientists,  part statisticians, part mathematicians, and part trend spotters. A data scientist is a unicorn who struggles and experiments with data to create as much impact as possible for optimal business decision making. The impact can be in the form of insights or data products or product recommendations. Here are some crazy definitions for a data scientist found on the web:

  • A statistician wearing a bow tie. – Hadley Wickham, Chief Scientist at R studio
  • A person who is better at statistics than any software engineer and better at software engineering than any statistician. – Josh Wills
  • Someone who can bridge the raw data and the analysis – and make it accessible. It’s a democratising role; by bringing the data to the people, you make the world just a little bit better. -Simon Roger, Data Editor, Google.

Image Credit: Twitter

What is a Machine Learning Engineer?

A machine learning engineer very much relates to a data scientist job role as both work with large quantities of dynamic data and perform complex data modelling using exceptional data management skills. However, a machine learning engineer develops self-running software to automate predictive machine learning models, unlike a data scientist who produces insights. Every time the software performs a task, the results are used to predict the outcomes of future tasks with a higher degree of accuracy. Machine learning engineers stand at the intersection of software engineers and data scientists with proficiency in both.

Data Scientist vs Machine Learning Engineer – What do they do?

What does a data scientist do ?

What data scientists do is make discoveries while swimming in data… [their] dominant trait is intense curiosity — a desire to go beneath the surface of a problem, find the questions at its heart, and distil them into a very clear set of hypotheses that can be tested. -Tom Davenport and D.J. Patil:

Image Credit : Big Data & Data Science :What does a data scientist do ?

A data scientist job role is multifaceted and does not just involve model building all day long. Some of the core responsibilities of a data scientist include –

  • Building a strong foundation for performing robust analytics by collecting datasets. In rare cases, there might already be enough data available to work on otherwise most of a data scientist’s time is spent in curating datasets from disparate sources because data is the lifeblood of data science
  • Data preparation and exploration( treating missing values, correcting data inconsistencies, identifying outliers, noise removal, and more) is the most important and time-consuming task performed to analyse the contents of a dataset and transform it into a usable format for advanced modelling
  • Data scientists do not use the entire dataset to build or train a model because doing so leaves them with no data to test the model before applying it on real-life scenarios. Having gathered sufficient amounts of data, splitting the data into training and test sets is an important task that every data scientist needs to perform before predictive modelling. Training set helps data scientists build and test the model while the test set helps evaluate the accuracy and performance of the model
  • Build machine learning pipelines and personalized data products to help a business’s bottom line
  • Refining the machine learning algorithms to maximize efficiency is an integral part of a data scientist’s job because the initial model is very basic and just outlines the expected outcomes
  • Effective communication and collaboration with the IT and business are important to prove that the models or data products that they have built have something of value for the business

What does a machine learning engineer do?

Right from personalized Netflix recommendations to customized web searches, machine learning engineers are the marvels who impact our day to day lives in various ways for the good. ML engineers do not explore data as much as data scientists do. They are mainly responsible for building a machine learning algorithm that can analyse the data and produce outcomes on any input dataset. Some of the core responsibilities of a machine learning engineer include –

  • Choosing the right training set for model development
  • Inputting data into the models that have already been defined by data scientists
  • Choosing the right machine learning algorithms based on business requirements and model compatibility
  • Focus on the machine learning infrastructure needed to bring the code into production: the infrastructure for training the model, the infrastructure at the inference time, and the infrastructure needed for labelling and annotation
  •  Collaborate with data engineers and data scientists to build data and model pipelines
  • Perform model tests and experiments
  • Communicate with stakeholders to clarify business requirements, analyse business problems, and identify the scope of the solution needed
  • Implement best practices to enhance the existing machine learning infrastructure of the organization

Data Scientist vs ML Engineer – Who earns more?

Data scientist vs machine learning engineer- while comparing salary, considering the broad responsibilities and diverse skills of a data scientist, it is obvious that they earn much more than machine learning engineers. India alone has witnessed a 400% increase in the demand for data scientists across multiple industries wherein there is a limited supply of skilled data science professionals. According to Glassdoor, machine learning engineers in India earn salaries in the range of Rs. 7, 50,000 to Rs. 15,50,000 while a data scientist in India earns somewhere between Rs. 10,55,000 to Rs. 20,52,000. However, there could be a few instances where machine learning engineers get paid more than data scientists and it entirely depends on the organisation and size of the project.

Source: Glassdoor

Source: Glassdoor

Where does the line of demarcation stand between data scientist vs machine learning engineer ?

The relationship between data scientists and machine learning engineers is like Tom and Jerry regardless of how many times they argue and discuss a project they won’t be apart. Their job roles and responsibilities are complementary to each other and supportive. You can think of the machine learning engineer as a general contractor while the data scientist is considered the architect. Both are equally important to build a strong structure. The skills of an ML engineer overlap with that of a data scientist but data scientists are more academic in nature with educational qualifications in the form of Master’s, PhD’s, or other advanced degrees in Math and Statistics. To the contrary, a machine learning engineer deals with disparity between an academic mindset and the necessity to apply it in production. 

Data scientists and machine learning engineers have very similar responsibilities – but it’s arguable that a data scientist has a few additional responsibilities than a machine learning engineer in terms of thinking of the data science process as a whole, including all the tasks right from data collection to presentation of the data for analysis and actionable insights. That’s not to say that a data scientist does more work than a machine learning engineer! Depending on the organization and the project, it’s likely that both job roles will require the same level of hard work and commitment for the best possible outcomes. 

After comparing data scientist vs machine learning engineer, It is clear that both data scientists and machine learning engineers offer high median salaries and have a strong job outlook. Having understood the differences, now you can decide for yourself whether you fit into a data scientist job role or a machine learning engineer job role. Regardless of the chosen career path, Springboard helps professionals emerge out as a winner through its comprehensive accredited data science and machine learning career track programs. These courses are self-paced, follow a project-driven course curriculum with 1:1 mentorship and comes with a job guarantee. If data is your thing then there is no time like the present to develop the mindset to upskill. Do not hang back and enroll for one of these programs to acquire the most in-demand tech skills and succeed in your career.