Data Analyst, Data Scientist, Software Engineer, Big Data Engineer, Machine Learning Engineer, Machine Learning Scientist–the world of ML and Data Science has a myriad of roles. 

Do the names seem similar and add to your confusion? 

Helping you through Interview Kickstart brings a series of comparison articles based on similar yet different professions. 

Generally dealing with computer, mathematics, and statistics background, there lies vast difference in each field known only to the individuals with knowledge and information. This article deals with the difference between data scientists and machine learning engineers. 

Here’s what we’ll cover:

• What is a Data Scientist?
• What is a Machine Learning Engineer?
• Roles & Responsibilities: Machine Learning Engineer vs Data Scientist 
• Skills: Data Scientist vs Machine Learning Engineer 
• Tools: ML Engineer vs Data Scientist
• Salary: ML Engineer vs Data Scientist
• Interview Kickstart: Data Scientist vs Machine Learning Engineer
• Frequently Asked Questions on Data Scientist vs Machine Learning Engineer

Who is a Data Scientist?

A Data Scientist is a professional who uses technologies to convert raw data into actionable insights and for the process of decision-making. They utilize statistical concepts, data mining, machine learning, and predictive analytics to aid in pattern and trend identification and anomaly detection. The services are available to a wide number of industries, allowing job opportunities for individuals holding expertise in different domains. 

Who is a Machine Learning Engineer?

A Machine Learning Engineer is a professionally trained individual who works to develop models for prediction-based results. They develop, optimize, and maintain algorithms that are further trained to encounter problems based on the training data. They serve as an important part of the data science team, and their actions are among the prime contributions to the development of Artificial Intelligence Systems. 

Roles & Responsibilities: Machine Learning Engineer vs Data Scientist 

Distinct professions come with distinct roles and responsibilities, which are stated separately for each. 

Machine Learning Engineer

• Generate plans to utilize the potential of Machine Learning to solve business problems, enhance the entire deployment workflow, and meet customer needs. 
• Research, modify, and apply data analytics and data science prototypes
• Utilize statistical models for model improvement and optimization 
• Train and retrain the Machine Learning models and systems 
• Deploy the ML and DL models for production 
• Optimize and expand the Machine Learning libraries and frameworks 
• Analyze the data using visualization and exploration techniques 
• Perform inference testing on different hardware such as GPU, CPU, and edge devices 
• Choose the best Machine Learning algorithms based on their applications 
• Locate training datasets from suitable data sources and databases
• Perform version control of experiments, models, and metadata 
• Constantly monitor, debug, and optimize the performance of developed programs or plans and applications 

Data Scientist

• Use Machine Learning tools for feature selection, creation, and classifier optimization. 
• Analyze the vast datasets to identify patterns and solutions at different ML lifecycle stages. 
• Develop customized models and algorithms as per client needs 
• Perform data mining or use extraction techniques to gain data from different data sources 
• Recognize the relevant datasets and generate synthetic data 
• Work on the data collection procedures while using information to develop analytic systems 
• Generate data annotation strategies and custom tools for optimizing the modeling workflow 
• Generate Machine Learning algorithms and develop prediction systems 
• Enhance the data integrity for analysis through various processing methods
• Propose strategies and solutions for tackling the business challenges 

Skills: Data Scientist vs Machine Learning Engineer 

The different sets of skills required for each profession are stated below. 

Machine Learning Engineer

Knowledge: Must be well-versed in advanced mathematics and statistics. It includes linear algebra, area of calculus, and Bayesian statistics. 

Degree: Advanced degree in computer science, mathematics, artificial intelligence, statistics, deep learning, or other relevant field. 

Programming languages: Working experience with coding and programming like Python, C, C++, Java, R, and JavaScript

Machine Learning: Practical understanding of Machine Learning frameworks, packages, and libraries. Understanding and ability to develop software architecture, data structure, and data modeling. 

Soft skills: Communication and problem-solving skills, time management, teamwork, and domain knowledge. 

Data Scientist

Degree: Bachelor’s and Master’s degree in data science, computer science, statistics, math, or related field 

Programming languages: Working experience with programming languages like Python or R and database query languages like SQL, Pig, Hive, C++, Java, or Scala. 

Knowledge: Application ability of distributions, regression, statistical tests, and maximum likelihood estimators, specifically to deal with data for results. Strong mathematical skills in multivariate calculus and linear algebra to handle algorithm optimization techniques. 

Machine Learning: Knowledge of Machine Learning methods such as Naive Bayes, Decision Forests, K-nearest neighbors, and Support Vector Machines. 

Data wrangling: Ability to handle data imperfections and make them worthy of use 

Data visualization and communication: Must be proficient in using ggplot, d3.js, matplotlib, and Tableau for visually encoding data. Additionally, one must be able to communicate the understanding to both technical and non-technical audiences. 

Soft skills: Must have hands-on experience with tools, problem-solving abilities, and a strong technical background. 

Tools: ML Engineer vs Data Scientist

Owing to a few similarities in skills, roles, and responsibilities, both professionals deal with some similar and some unique tools. Here is the comprehensive list of both. 

Common Tools of ML Engineer and Data Scientist

The common requirements are:

• Programming Languages: Mainly Python and R 
• Data manipulation and analysis: Pandas and NumPy
• Version control: GitHub and Bitbucket
• Cloud: AWS/GCP/Azure 
• Metadata storage: Neptune.ai, Weights & Biases, Comet.mi

Tools Specific to Data Scientists

• Statistical analysis: Statsmodels 
• Big Data tools: Apache Spark 
• Visualization: Matplotlib and Seaborn 
• Experimentation: Apache Airflow

Tools Specific to Machine Learning Engineers

• Machine Learning libraries: Scikit-learn, PyTorch, and TensorFlow 
• Model deployment in Cloud Environments: TensorFlow Serving, MLflow, and Docker 
• Feature engineering: Featuretools 
• Model monitoring and management: Prometheus and Grafana
• Serving: TensorRT, ONNX, TFServing and TorchServe

Salary: ML Engineer vs Data Scientist

The insights into salary breakup for both professions are as follows:

The Interview Kickstart Contributions to Data Scientists & Machine Learning Engineers

Both professions have an increasing demand owing to technological advancements and efforts in the development of AI. The difference lies in the area of focus. A data scientist is concerned more with exploration and data analysis for extracting insights and informed decision-making. On the other hand, a Machine Learning engineer emphasizes the deployment of Machine Learning models to ensure efficiency, scalability, and seamless integration with software systems. 

Choosing any of the two fields depends on one’s career goals, prior experience, and other factors. However, preparation requires knowledge from industry experts and personalized guidance. Enroll in our Data Science Masterclass if you are aiming for a data scientist position. Bag the opportunity to learn from Principal and Research Data Scientists from tier-1 companies.  

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Frequently Asked Questions on Data Scientist vs Machine Learning Engineer

Q1. Do Machine Learning Engineers need to know Data Science?

Ans. Machine Learning Engineers benefit from a strong foundation in Data Science for data manipulation, analysis, and preprocessing. However, learning it first is not mandatory. 

Q2. Should I learn Data Science or Machine Learning first?

Ans. The need to learn one before another depends on the requirements of the project one is dealing with, career goals, or other similar reasons. Learning Data Science first provides an understanding of data manipulation, visualization, and statistical analysis, while learning Machine Learning first provides insights into predictive modeling and algorithmic aspects. 

Q3. Will Machine Learning Engineers Replace Data Scientists?

Ans. The roles are different and complementary. However, replacement with each other is out of the question. 

Q4. Which is more in demand: Data Science or Machine Learning?

Ans. Machine Learning Engineer tops the list of Best Jobs in 2023 from Indeed. The US has also listed Machine Learning Engineer’s jobs to be at the top compared to that of Data Scientists, making it a more demanding option. 

Q5. Can I switch my career from Data Scientist to Machine Learning Engineer?

Ans. Yes, it is possible to switch careers from Data Scientist to Machine Learning Engineer. It can be done by filling in the knowledge and skill gap through preparation from online sources or other educational institutions. 

Q6. Compare the following: ML engineer vs Data Scientist, ML Scientist vs ML Engineer, and Data Scientist vs ML Engineer. 

Ans. The comparison among each of these is 

ML Engineer vs Data Scientist: The ML Engineers deploy and optimize Machine Learning models while Data Scientists emphasize Exploratory Data Analysis. 

ML Scientist vs ML Engineer: ML Scientists are concerned with the R&D of novel algorithms, and ML Engineers are concerned with the implementation and scaling of these algorithms. 

Data Scientist vs ML Engineer: Data Scientists analyze and interpret complex datasets for decision-making while ML Engineers design and integrate ML models. 

Q7. How much does a Data Scientist and Machine Learning Engineer make at Google?

Ans. Google Data Scientists’ typical salary is around INR 16.5 lakhs per year, while that of a Machine Learning Engineer’s salary is around INR 17 lakhs per year.