There is a lot of confusion on the roles of a machine learning engineer vs data scientist, mostly because the roles are somewhat novel.
A machine learning engineer feeds data into a model defined by a data scientist and is by definition a software engineer with a specialization in machine learning.
A data scientist focuses on the research and statistical analysis required for a decision on the best machine learning method to use, then model and prototype an algorithm for testing.
The Growth of the Data Science Industry
The data science industry burst into the limelight in 2013, and since then, has been evolving and separating into different and more distinct roles. This growth caused a great deal of upheaval and unclear career functions. An example being various titles with similar roles, or vice versa, similar titles, but with different roles.
Before we define what a machine learning engineer and a data scientist do, it is worth noting that machine learning is a subset of Artificial Intelligence (AI). The term AI was coined in 1956 by John McCarthy for discussing and developing the “thinking machines” concept. This included:
- Complex information processing
- Automata theory
Almost sixty years later, AI is now considered as a computer science sub-field where computer systems are built to perform tasks that do not require human intervention such as:
- Speech recognition
- Language translation
- Visual perception
AI enables machines in reason execution by the replication of human intelligence. Since the primary objective of machine learning is teaching machines from memory and experience, feeding the machines the appropriate data is crucial. AI experts heavily depend on natural language processing and deep learning to aid the machines to identify inferences and patterns.
Simply, machine learning is a branch of AI where data-driven algorithms allow software apps to predict outcomes almost accurately with no programming. This is how apps like YouTube, Netflix, and Amazon can predict your preferences and suggest items you would like.
On the other hand, data science can be defined as the: “Description, prediction and causal inference from both structured and unstructured data.” This allows businesses and individuals to make more sensible and fulfilling business decisions.
Data science studies data origins, its representation, and the ways it can be changed into a valuable and usable resource. To make the data useful, a substantial amount of data has to be mined for the identification of useful patterns in business to:
- Gain a competitive edge
- Identify fresh markets
- Improve efficiencies
- Reduce costs
Machine Learning Engineer Vs Data Scientist Roles
There are a few similarities between the roles of a machine learning engineer and a data scientist. If you evaluate the two roles in the context of members of a team, data scientists handle the statistical analysis needed to decide the learning method to be used. The data scientists model an algorithm and a prototype for testing.
The machine learning engineer at this point takes the prototype and puts it to work at scale. The machine engineers do not have to understand the underlying statistics of the predictive models as a data scientist should.
However, the machine engineer programmers have to be conversant with all the software tools that make the models work.
What does a Machine Learning Engineer Do?
Machine learning engineers sit poised at the software engineering and data science intersection. They use programming frameworks and big data tools to ensure the gathered data from the pipelines is redefined and recognized as data science models by data scientists, ready to be scaled as necessary.
The machine learning engineers take the redefined data and feed it into models identified by the data scientists. The machine learning scientists are tasked with handling theoretical data science models and scaling them up to production levels that can take substantial real-time data.
Machine learning engineers create programs that control robots and computers. The algorithms the engineers develop enable a robot or computer to learn patterns in its internal programming data. Via this data, the machine learns how to think and understand commands- thus machine learning.
Types of Machine Learning
As an ML engineer, there are some types of ML you should be aware of:
1. Supervised Learning
In this type of training, the machine is trained using well-labeled data. This means some of the data is tagged in advance with the right answer. This algorithm is one that learns from labeled data, which helps to predict outcomes for unknown data
- Regression: This technique uses training data to predict a single output value
- Logistic: This method estimates discrete values that are based on a pre-set variable. This helps in predicting the probability of an event’s occurrence by fixing data to a logical function.
2. Unsupervised learning
Unsupervised learning is an algorithm that draws inferences from datasets with input data, but no labeled responses. The most common is cluster analysis, which is utilized for exploratory data analysis to uncover hidden groupings or patterns in data. The cluster modeling is done via a similarity measure, which uses metrics such as probabilistic or Euclidean distance.
3. Semi-supervised learning
These algorithms are a cross between supervised and unsupervised algorithms. They operate on data with very few labels but are largely unlabeled. A semi-supervised machine learning algorithm makes use of a limited labeled sample for self-training, of which the result is a partially trained model.
4. Reinforcement Learning
Reinforcement learning (RL) is more challenging and consists of learning through interaction and feedback. This is more of a trial and error learning algorithm where there are no labels.
The Machine Learning Steps
To understand the role of ML engineers and data scientists in organizations, we need to know what the development process looks like. Most of the time, ML projects follow these six steps, also called the Data Science Lifecycle.
1. Problem definition.
2. Data collection and preparation.
3. Model development.
4. Model testing.
5. Machine learning service deployment.
6. Machine learning service management.
Data scientists are critical in stages 1 through 4, while ML engineers are critical in steps 5 and 6.
What Does a Data Scientist Do?
When your business has a problem that needs solving or an answer to a client’s question, you get a data scientist for gathering, processing, and deriving insights from collected data. When a data scientist is hired in an enterprise, they look at every angle of your business. They then create programs via programming languages like Java for performing dynamic analytics.
The data scientists use different methods such as online experiments for helping your business grow. They also produce personalized and customized data structures that help you to understand your business and your clients, which helps you make better decisions.
Machine Learning Engineer vs. Data Scientist Role Requirements
These two roles require some strong academic requirements.
Machine Learning Engineer Skills
For one to be hired as a machine learning engineer, an individual needs at least a Computer science master’s degree, or even a Ph.D. However, there is a significant talent shortage, and most employers are okay with making a few exceptions, and allowing the candidate to learn hands-on and maybe watch some tutorials to get a better grasp of things.
For a potential candidate to stand a chance, there are some standard requirements such as familiarity with packages and the average machine learning algorithms, which are available in API libraries. According to IBM, ML engineers should know the following languages as ranked:
- Strong software development background, great knowledge of best practices in engineering and DevOps.
- Some experience working with a Container eco-system e.g. Mesos, Kubernetes, and Docker. If a company’s development team uses containers, they might demand these skills.
- Experience and a good understanding of Natural Language Processing
- Understand the use of deep neural networks (RNN [GRU, LSTM, CNN]), but only if the potential employer intends to develop NLP solutions.
- If the prospective employer uses GPU (Graphical Processing Units) such as NVIDIA, you need some experience in using profiling/ low-level optimization, Cuda/CuDNN GPU
Data Scientist Skills
Any individual interested in becoming a data scientist should acquire some knowledge in analytics, programming, and domain. Having the following skillset to your resume helps you become a better data scientist and have a cutting edge in the market.
- A Master’s or Ph.D. in engineering, computer science, statistics, or mathematics
- Excellent knowledge in SAS, R, Python, and Scala
- Experience and knowledge in SQL database coding and have experience using machine learning frameworks such as PyTorch and TensorFlow
- Ability to handle unstructured data from social media and videos
- Understanding the various analytical functions
- Machine learning knowledge
- Have experience working with BigData ecosystem, ETL (Hadoop, Spark/PySpark or Hive)
- Understand statistical techniques and data mining algorithms
- Strong analytical and communication skills
- Understand machine learning and data analysis libraries (Scipy, Scikit-Learn, Pandas, Matplotlib, NumPy, etc.
- Proficiency in at least one programming language (Scala, C++, Python)
- Knowledge of public cloud machine learning tools from Google Cloud, Azure, and AWS
- Be familiar with machine learning topics such as supervised and unsupervised learning, optimization algorithms, tree-based models, and dimensionality reduction
- Experience with programming tools such as MATLAB.
- Understanding of data visualization tools such as Power BI or Tableau
Machine Learning Engineer Vs. Data Scientist Responsibilities
Machine Learning Engineer
The responsibilities carried out by a machine learning engineer are dependent on the current project. If you look at different job postings, you may realize that most machine learning engineer posts include building algorithms underpinned by statistical modeling procedures and the maintenance of scalable ML solutions.
The main ML engineer responsibilities include, but are not limited to the following:
- The development of machine learning models
- Collaborating with data engineers and development of model and data pipelines
- Application of ML and data science techniques
- Writing production codes and bringing the codes to production
- Engaging in code reviews
- Implementation of machine learning libraries and algorithms
- Oversee the lifecycle from research, design, experimenting, development, deployment, monitoring, and maintenance.
- Improvement of existing ML models
- Keeping the business leaders up to date with complex processes
A data scientist is better versed in programming than an ML engineer. The data scientist is not as good at programming as a software engineer, where, in this case, the data scientist is better in statistics than in code.
Data scientists can store and clean vast data amounts. They peruse the data sets to build predictive models, identify crucial insights, and carry out the running of data science projects from one end to the other. Most data scientists started as data analysts. Some of the responsibilities of data scientist jobs include:
- Researching and developing statistical models for thorough analysis
- To understand the needs of the company and come up with possible solutions by teaming up with engineering departments and product management
- Convey statistical concepts and results to business leaders
- Optimize development efforts by using the appropriate project designs and databases
- Development of algorithms and custom data models
- Build tools and processes to allow for performance analysis and monitoring and data accuracy
- Make use of predictive modeling to improve customer experience, ad targets, revenue generation, etc.
- Test model quality and the development of A/B testing framework
How Machine Learning and Data Science Work
Since machine learning is an AI form, it enables software apps to predict outcomes with precision using data-driven algorithms.
Some of the ways machine learning works by mechanisms that detect fraud in banks, financial institutions, and other enterprises. An algorithm detects an unusual pattern like a credit card being swiped outside its normal geographical area, and quickly sends an automated alert that blocks the card.
A machine can identify the pattern and block the card faster, which means the fraud timeframe shortens, which saves both the client and the bank a lot of money.
Data science comprises of data examination, its origin, and its analysis. Since data scientists collect data, analyze, turn it into a fit-to-use format and point out patterns in the data. The goal is the identification of trends that help business people make smart business decisions.
One of the ways data influences decisions is personalized healthcare. Hospitals can use data science to minimize re-admission rates which lead to extra costs in terms of manpower and resources that are avoidable. The collection of patient data by hospitals helps them to identify such factors as their patients’ residential area or income level. These factors identify the chances of the patients’ risk level of returning for re-admission, and the data analysis detect certain patterns.
For example, the risk of re-admission may be high in patients from a certain area because there might be no pharmacy or clinic in the area, and patients keep getting re-infections, thus re-admitted. A hospital administrator can use this data analysis to figure out how to provide tailored healthcare for these residents.
Salary of Machine Learning Engineer Vs. Data Scientist
Machine learning engineers and data scientists do not have a universal salary. The average salary depends on the candidate’s skills, company size and resources, and the candidate’s preferences. Some companies pay tons of money without attracting a candidate.
Some companies like Google Brain can easily get individuals to work for free as long as they are working there, which says a lot about a company. By estimation, a machine learning specialist with two to five years’ experience may earn $140k, while a data scientist may earn $110k on average.
- Glassdoor- $114,121
Entry-level ML engineers start with approximately $93k, while more experienced engineers can earn more than $180k a year
Approximate Data Scientist Annual Salary According To:
Entry-level Data Scientists earn an annual salary of approximately $89K, while Senior-level professionals can earn more than $143K annually.
The data scientist earns the least of the two because he or she is not as independent as an ML engineer. The ML engineer delivers by designing a company’s application logic and data architecture, as well as delivering the AI model.
The data scientist designs the model which cannot be used minus the architecture, and therefore cannot be deployed. This does not make the data scientist obsolete, but because he or she focuses on ML neural networks and development, he or she has the most knowledge in these areas.
This article is a showcase of Machine Learning Engineers Vs Data Scientists in terms of what they do, their qualifications, the similarities and differences, and their pay. If you intend to study for either profession, you know what to do, and the qualities companies look for in a candidate
As a business, you know where and how data science and machine learning come in, and how the two complement each other. To have a cutting edge in the business world, you have seen how to use the services, like in banking and healthcare.
Companies are now looking to work with professionals with the know-how to go through the maze of raw data they collect daily and help them make sense of it and make business decisions. In some quarters, this data collection is looked at as an invasion of privacy. For businesses and any data statistician, the data is a livelihood.
IBM predicted that in 2020 the number of vacant positions in the US for data professionals would rise by 364,000 to 2,720,00 openings. In this 21st century, AI is bigger than ever before, and before the next century, we will have made vast leaps and bounds