No one will argue the importance of data scientists and other data-related job titles to enterprises today. But when it comes to getting the most from your data team, it’s critical to understand what each role can and cannot do for the business.
Of course, the specifics of every role will vary based on industry, company size, and a host of other criteria. Nevertheless, it’s important for CIOs to be familiar with each function’s key responsibilities and to understand how the various roles fit with each other and with the organization’s data goals. To that end, below is a brief overview of key positions on the data science team.
The Data Analyst
Data analysts have been helping organizations extract meaningful information from their data long before it was cool. Today, data analysts must be familiar with business intelligence tools and are involved in integrating data from many different sources. In most organizations, data analysts’ work will overlap with data scientists and data engineers.
The Data Engineer
The data engineer’s primary responsibility is to make sense of messy data so that data scientists can then utilize it. Typically, this role involves a significant amount of data prep and hygiene work, including lots of ETL to ingest and clean data. Data engineers should be able to work with scripting languages like Python, SQL, and Spark. In addition, they will need programming language skills to identify and clean up data problems.
The Data Scientist
Data science is a broad field that can include data analytics and data processing, but the core concentration is the application of predictive techniques to data using statistical machine learning or deep learning. Data scientists require domain expertise in statistical machine learning, random forests, training, model evaluation and refinement, data normalization and cross-validation, and more. Given the depth and breadth of required skills, it’s easy to see why data scientists are so highly sought after and valued.
The Machine Learning Engineer
The machine learning engineer is essentially a jack of all trades, architecting the entire process of machine and deep learning. Part of this role is knowing the applications, understanding the downstream data architecture, and honing in on system issues that may arise as projects scale. A machine learning engineer typically must also be skilled in infrastructure optimization, cloud computing, containers, and databases. They must collaborate closely with their data science and deep learning colleagues to regularly reassess models and avoid model drift.
The Deep Learning Engineer
A deep learning engineer is a data scientist who is an expert in deep learning techniques. Their work is part data science and part art to develop what happens in the black box of deep learning models. Deep learning engineers will need to process large datasets to train their models before they can be used for inference, where they apply what they have learned to analyze new information.
For more on these roles and what you need to know to get the best out of your data science team, check out this eWeek article.