See how Wallaroo.AI helps unlock AI at scale for Retail >

Data Scientist vs Machine Learning Engineer

Data Scientists vs Machine Learning Engineers

The role of Data Scientist has become popularized over the last decade or so. Data Scientists usually have a background in statistics, math, and computer science; however, these roles were not truly versed in the infrastructure side of things, and therefore, could not go from inception to production with their machine learning models without time and resource constraints. Ergo, the role of Machine Learning Engineer spawned. ML Engineers are veteran software engineers that possess skills in building ML workflows and infrastructure that are required to move projects to production. 

Role Of A Data Scientist

When a business has a problem that needs resolving, they turn to Data Scientists to gather, analyze, and obtain valuable insights from data. This role does not usually provide production-ready code as it is not their background. Being a Data Scientist is a bit more ad hoc and seeks to translate the business problem into a more technical model to help drive business decisions. 

  • Data Scientist’s Responsibilities:
    • Understand how to translate business needs into data-oriented solutions
    • Produce reports and presentations of research, findings, and insights to key business leaders
    • Develop custom data models and algorithms
    • Identify what data sources/measurements might be appropriate to solve specific analytical problems, and/or recommend what other processes or data sources the organization should be measuring in order to meet specific goals
    • Help the business achieve organizational goals, for instance, increase revenue, exploit new growth areas, or acquire new customers
    • Develop A/B testing framework for continuous testing of model quality

Role Of A Machine Learning Engineer

This role is a crossroad between data science and software engineering. These engineers are responsible for integrating tools and frameworks together to ensure the data, data pipelines, and key infrastructure is working cohesively to allow for ML models to be productionized and scale as needed. These engineers also have the capability to automate repetitive tasks as well as build algorithms that allow systems to identify patterns within their own data to teach themselves how to think.

  • Machine Learning Engineer’s Responsibilities:
    • Develop data and model pipelines
    • Design distributed systems
    • Write production-level code
    • Perform code reviews
    • Enable ML projects to run in production and scale
    • Execute on ML algorithms, frameworks, and libraries 

Collaboration Between Data Scientist & ML Engineers

Having a team of Data Scientists and ML Engineers can be advantageous to larger enterprises. The reason is, most of the entire life cycle of any analytics and ML project consists of tasks that require both roles to complete the project successfully. No one role can answer these key questions when building an ML track:

  • How do we convert a business problem into a data science problem?
  • For our project, do we have the data, infrastructure, and pipelines required?
  • How do we measure if the data quality is good enough?
  • What is our deployment strategy?
  • What metrics are we using to measure the success of the model?

The full cycle from start to finish begins with the Data Scientist extracting data and beginning preparing that data, building the models, and training, testing, and validating those models. Once the Data Scientists complete their stage of the process, the ML Engineer deploys the models into production and sets up the system for continuous iteration, auditing, and monitoring. Together, these roles provide the business with high-quality work and best practices.

Data Scientist vs Machine Learning Engineer: The Bottom Line

You will find much confusion over these two roles due to the fluidity of how companies decide to define each of these roles by what they need rather than focusing on what each role is meant to be responsible for, but at a high level, Data Scientists should be focused on analyzing data, providing insights, and building models, while ML Engineers should be focused on productionizing and deploying for large-scale complex machine learning products. 

About Wallaroo. 

Wallaroo enables data scientists and ML engineers to deploy enterprise-level AI into production simpler, faster, and with incredible efficiency. Our platform provides powerful self-service tools, a purpose-built ultrafast engine for ML workflows, observability, and an experimentation framework. Wallaroo runs in cloud, on-prem, and edge environments while reducing infrastructure costs by 80 percent. 

Wallaroo’s unique approach to production AI gives any organization the desired fast time-to-market, audited visibility, scalability – and ultimately measurable business value – from their AI-driven initiatives, and allows data scientists to focus on value creation, not low-level “plumbing.”

Table of Contents



Related Blog Posts

Get Your AI Models Into Production, Fast.

Unblock your AI team with the easiest, fastest, and most flexible way to deploy AI without complexity or compromise. 

Keep up to date with the latest ML production news sign up for the Wallaroo.AI newsletter

Platform Learn how our unified platform enables ML deployment, serving, observability and optimization
Technology Get a deeper dive into the unique technology behind our ML production platform
Solutions See how our unified ML platform supports any model for any use case
Computer Vision (AI) Run even complex models in constrained environments, with hundreds or thousands of endpoints