Data Science vs. Machine Learning Engineer: Which Career is Right for You?

Data Science and Machine Learning are two of the most promising career paths in the rapidly evolving tech-driven landscape. You can explore the various career paths that these two disciplines offer, depending on your skill sets and interests.

If you enjoy delving into big data and driving key decision-making in companies, then Data Science might be your calling. On the other hand, if you’re passionate about making innovative systems, facilitating learning and development, then Machine Learning is right for you.

If you’re considering a career in Data Science vs Machine Learning Engineering, it’s important to understand the distinctions between the two, from skills and learning paths to job prospects. This blog covers everything you need to know.

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Data Science vs Machine Learning Engineer: Key Differences for Canadian Professionals

Exploring the difference between a Data Scientist and a Machine Learning Engineer? Find out about job responsibilities, required skills, job opportunities in Canada, etc, right here:

Here’s a more detailed look at the job roles, skills, tools, and job opportunities associated with Machine Learning and Data Science. Read on to understand which one aligns with your interests better.

Role Overview & Day-to-Day Work

Both Data Science and Machine Learning play crucial roles in backing Artificial Intelligence, but both entail different responsibilities.

A Data Science workflow typically involves the following processes:

• Hypothesis Development.
• Data Collection.
• Data Processing.
• Data Analysis.
• Data Modeling and Evaluation.
• Reporting and Visualization.

Machine Learning, on the other hand, involves the following responsibilities:

• Data Collection.
• Data Preprocessing.
• Identification of patterns in data sets.
• Parameter tuning.
• Model testing.

While both workflows contain similar steps, the Data Scientist’s job ends with presenting the findings to stakeholders. At the same time, the Machine Learning Engineer continues to monitor and maintain models after they are in production.

Required Skills & Tools for Each Role

Both Data Scientists and Machine Learning Engineers use a combination of high-tech methods and tools to fulfill their day-to-day work. Here’s a quick look:

Data Scientists

• Python.
• R.
• Machine Learning Algorithms.
• Jupyter Notebooks.
• Hadoop.
• Soft skills, including communication and collaboration.

Machine Learning Engineers

• Python frameworks such as NumPy, pandas, Scikit-learn, Matplotlib, Seaborn, and PyTorch, among others.
• Software skills.
• Technical design.
• Git and GitHub.
• Kubeflow.
• Neptune AI.

Educational Background & Learning Path

What educational background do you need to have to ace Data Science and Machine Learning roles? Here’s a quick look:

Data Science:

• Typically involves an undergraduate degree in the fields of statistics, information science, or computer science.
• Master’s or doctoral degrees with Data Science or Machine learning as a core focus.
• Relevant industry bootcamps and certifications.

Machine Learning

• Bachelor’s Degree in Computer Science, Maths, or Statistics.
• Master’s and Doctoral degrees.

Job Opportunities in Canada

Canada, like any other developed nation, offers advantageous career opportunities for budding Data Scientists and Machine Learning Engineers. Here are the popular job positions in both disciplines:

Data Scientist

• Guest Data Scientist.
• Senior Data Scientist.
• Metadata Analyst.
• Senior Marketing Data Scientist.
• Data Engineer.
• Business Intelligence Analyst.

Machine Learning

• Platform Engineer (Machine Learning Infrastructure).
• Data Scientist- Machine Learning.
• Research Machine Learning Scientist.
• Entry Level Machine Learning Engineer.
• AI Engineer.
• Head of AI.

Career Growth

Both Data Scientists and Machine Learning Engineers have exciting career paths accommodating excellent growth and specialization opportunities:

Data Scientist

• Entry Level- Junior Data Scientist or Data Analyst.
• Mid-Level- Data Science Team Lead or Senior Data Scientist.
• Advanced Level- Head of Data Science, Principal Data Scientist, or Chief Data Officer.
• Specialization – Quantitative Analyst, Data Engineer, or Business Intelligence Analyst.

Machine Learning Engineer

• Entry Level – Junior AI Developer or Machine Learning Engineer.
• Mid-Level – Senior ML Engineer or Machine Learning Team Lead.
• Advanced Level – Chief AI Officer or Head of AI.
• Specialization – MLOps Engineer, Computer Vision Engineer, or NLP Specialist.

Also Read: How Canadian Business Leaders Can Use Machine Learning

Factors to Consider When Choosing Between Data Science and Machine Learning Engineering

How do you make an informed decision when it comes to choosing between a Data Science or Machine Learning career? Consider the following factors closely before reaching a decision:

• Interests (Research vs Engineering, coding vs storytelling, etc.)
• Job Opportunities
• Salaries
• Future Prospects

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FAQs on Data Science vs. Machine Learning Engineer

Q: What is the main difference between a data scientist and a machine learning engineer?
Ans: A Data Scientist primarily uses various methods, such as data collection, wrangling, mining, and analysis, to offer meaningful insights to businesses, helping them make key decisions. An ML engineer, on the other hand, designs, deploys, and maintains the production of machine learning models.

Q: Which role pays more in Canada—data science or ML engineering?
Ans: Machine Learning Engineers earn a little more than Data Scientists in Canada. The average annual salary of a Data Scientist in Canada is CAD 1,01,207, while it is CAD 1,15,940 for a Machine Learning Engineer.

Q: Can I transition from data science to machine learning engineering?
Ans: Yes, you can definitely transition from Data Science to Machine Learning Engineering since both roles have a lot in common (like skills and programming languages required, etc). Focus on areas like software engineering fundamentals, cloud computing, and DevOps practices for targeted upskilling.

Q: How do I build a portfolio for data science or ML engineering?
Ans: Follow a few common tips to create Data Science and ML Engineering portfolios:

• Be authentic.
• Tell a story (especially for Data Science).
• Distinguish between content and code-based portfolios.
• Don’t include cookie-cutter projects.

Q: What programming languages should I learn for both careers?
Ans: Some of the programming languages that you should learn for both Data Science and Machine Learning careers are Python, R, SQL, C++, and Java, among others.