Companies understand how important data is to operations and digital transformation and are eager to bring expertise into their departments. However, hiring a “data scientist” might not get exactly the person they need in the position they have in mind. We know that data science roles have diversified quite a bit, so read our quick-start guide to hiring data science roles to avoid unnecessary resumes, poor hiring decisions, and a lot of heartache.
Data science has evolved—and so have the roles under its umbrella
Data science began as an interdisciplinary field drawing on mathematics, computer programming, and data/business analytics to find better insights—faster—from all the data we’re generating. Now, that role has evolved to many more specific niche roles designed to address the sheer complexity of the data science field. Here are a few you should know for your business.
Data scientists extract meaning from data. They’re generalists, working in research or business but focusing on the theoretical possibilities inherent in data. They build pipelines for analysis and, on smaller teams, conduct some analysis themselves. If you have a team of one or are building a data science department, a data scientist position might be an excellent place to start because they’re typically a jack of all trades.
Some required experience and knowledge include mathematics and statistics, some programming, and knowledge of and experience creating data models.
Data engineering positions began surfacing as data science projects became more complex on the business side. Data engineers handle the framework for data science—think building pipelines, preparing data, cleaning data, and building databases. An essential part of this position is a deep knowledge of the programming designed to build, maintain, and (most importantly) troubleshoot these data environments so that data scientists can focus on extracting value.
Data engineers set the tone for the entire data science process. They make critical decisions about which tools to use and when. They keep up with security best practices. They build the playground that allows data scientists and analysts to play with data and drive value from it. The position also includes some common subsets.
Although very similar to a data engineer, a data architect designs the vision for an organization’s data framework. They create a blueprint that includes the long-term goals of a data science initiative, where a data engineer executes that vision in practice. In small teams, this might be the same position.
Storing data is a field nearly its own. Database architects and administrators design the framework for how companies will store and retrieve valuable data and how to keep it safe. For larger teams with more complex data needs, they are critical to data scientists and other analyst positions because they help make the data available.
Machine learning engineer
In bigger departments, roles can niche down further. Machine learning engineers are similar in spirit to data engineers but spend time researching, building, and designing self-running ML systems. They focus on building predictive models and, in some cases, creating the beginnings of AI itself. This career also has several even more specified subsets.
Deep learning goes one step further than machine learning. In machine learning, developers use algorithms to program machines to perform tasks automatically with minimal to no human intervention. In deep learning, highly complex algorithms modeled on the human brain teach machines to think like humans. This skill is good for businesses with highly complex, varied challenges that require more than automation.
Natural Language processing
Natural language processing also teaches machines to think like humans—but specifically in terms of language. It uses models to demonstrate human language tasks so that machines can understand different forms of human communication—think text messages, social media posts, and even transcripts of customer service calls. It also teaches machines to respond to humans in human-like ways.
Artificial Intelligence engineering
AI requires complex frameworks to operate. An AI engineer is also like a data engineer in spirit but focuses on building, maintaining, and troubleshooting the frameworks and environments required to develop and deploy AI in a business setting. It takes AI and deploys it in real-world settings, not theoretical experimentations.
Need someone to help with basic analytics in the broadest sense? That’s a data analyst! Data analysts can also help multiple teams (marketing or business development, for example) and often have strong communication and data visualization skills. This position focuses on business data, business questions, and business outcomes to enable data-driven decision-making.
Hiring for your first (or next) data science position
One of the best things you can do for your business is to keep an eye out for new skills and position types. This helps narrow down the correct information for job ads and ensures you get the skillset you need when you make your data science hire.
Ai+ Careers can help your business determine the best type of position for your business needs when it’s time to hire data science talent. We have the expertise and the network to help make your hiring efforts count.
We’re ready to help with careers, both finding a job or finding a new employee. With connections on both sides of the equation, we can help your business find the right talent faster. Our recommendation engine, another form of artificial intelligence, helps sort resumes and make contacting the right employee easier—much more manageable than wasting hours sorting through Indeed resumes.
Contact us to find out how we can help you secure the right candidate or explore our existing talent database to begin your search. We are ready to help you find your perfect data science match.
Your business is ready to dive into your data, but you’re not sure what you need. Learn more about what data science role is best for your organization here!