You understand the importance of data and have committed to making it a differentiator within your organization; or maybe you’ve heard about AI and finally decided it’s time to make the jump. We’ve noticed a lot of confusion out there about the role data science plays in the business and what expertise is required for which project.
[UPDATE] Indeed recently published on this topic. They leverage their own behavioral data to describe trends in the field and definitions for data science roles. Click here to learn more.
Data science is commonly generalized by many organizations. However, there are different roles that may or may not be useful depending on each specific project.
In this post we hope to shine a light on some of these roles and how they interact with each other. We will move from roles that can be found in most organizations towards specialized AI roles used in organizations.
Data analysts query and summarize data. The analyst’s goal is to tell a story and produce actionable insights. This can be as basic as aggregating data on Excel and creating a chart. It’s the analyst’s job to clearly communicate the insights with visualization tools.Analysts don’t necessarily have strong math or statistics backgrounds, but they can interpret the data and find trends. Data preparation and visualization are of utmost importance (e.g. creating dashboards with KPIs) and they can further add value by establishing data management practices. A data analyst would come in at the end of an AI project, once all the data was gathered and processed, and try to discover any trends and present insights in a more informative way.
In order for analysts to use the data, data engineers must maintain the appropriate systems. A data engineer designs, constructs, installs, and tests the architectures where data is stored. They build data systems according to company needs, depending on the amount and type of data that needs to be stored. This requires more in-depth knowledge of database technology and programming.In an AI project, data engineers would come in right at the beginning to help decide many questions: What kind of data will we be storing? How often will we query the data? How much data do we expect to collect? How scalable does the system have to be?
Data scientists apply advanced mathematics and statistics concepts to data analysis, creating statistical models to analyze large amounts of data. They are expected to have strong programming skills in order to design algorithms that manipulate data and must have a good understanding of data infrastructures and data analysis. Since new analytical methods are being developed, data scientists must stay up to date with the latest research and technologies.
Machine learning engineer
You can think of a machine learning engineer as a subset of a data scientist. An ML engineer has a more practical focus and is able to implement custom AI models despite a more limited knowledge of the theory behind the models. So, while they have very strong programming skills and are ready to implement an AI solution at a moment’s notice, they may not have such a deep understanding of statistics and math.
Here is an example.
Let’s say you have an ecommerce website where you sell clothes. If the site doesn’t already have data storage in place, you can go ahead and call the data engineer who will build the appropriate database infrastructure to gather and store the data. The data analyst can then query the data, making tables, histograms, and charts to show how sales have changed over time. The analyst can also set up some really cool dashboards that automatically update. You might view these dashboards each week to see if the company is on the right track and to make important business decisions.
Your data scientist can then come in and make a more sophisticated analysis, creating models to predict future sales growth based on many different variables. Maybe a specific model is important for the solution you want to implement. You can then call the machine learning engineer to implement a product recommendation system. This system analyzes shopping behavior on the website, compares shopper profiles, and recommends products with a high accuracy that drives sales further.
Keep in mind that the industry is constantly evolving and staying up to date with new roles is important as new technologies are developed.
At KUNGFU.AI we help companies sort this all out. Once we help develop your data and AI strategy, we can provide the correct expertise to knock out projects. For more information on how AI can aid your organization, check out our blog post on driving ROI with AI.