Authors: Derek Plemons & Jesse Henson
There is a cliché in the data science industry. Maybe you’ve encountered it. Every company is searching for their unicorn, their jack-of-all trades data science professional. This wizard can do it all: data analysis, data engineering, data science, and software engineering. These people do exist (we happen to work with a few), but there are pros and cons to looking for this magical, one-size-fits-all data science superstar.
In fact, most companies would see more benefit from hiring a full-fledged data science team.
Given that data science and machine learning is such a new field of expertise, there is a great deal of confusion around what a data science team looks like and the roles of its members. The size, structure, and roles of a data science team will vary from company to company and project to project.
How you structure your initial data team can determine the success or failure of your project. Rather than letting project outcomes rest upon a single data scientist’s shoulders, embracing the collaborative, specialized, and need-based data science team approach can yield improvements in efficiency, effectiveness, and ultimately, in business value.
Who Are the Members of a Data Science Team?
We aim for teams only as large as needed to solve a problem. The scale and scope of the solution will not only determine how many team members are necessary, but which skillsets to prioritize. A few common roles on a primary data team are:
A business analyst aims to make business processes more efficient using technology. They gather requirements and document practices. Business analysts evaluate a need and develop systems to solve the problem. Their tasks may vary from building documentation to cost-benefit analysis depending on the business need.
Data analysts study the data. They are focused on finding patterns in data to solve a problem. They turn data into insights. They may perform tasks such as collecting, storing, interpreting, visualizing, and presenting data analysis insights into meaningful stories.
A data engineer specializes in implementing solutions to get the data where it’s needed. They maintain, build, and design systems for the rest of the data team to get their data. Often in conjunction with a data analyst or data scientist, data engineers gather data from sources and improve upon data quality and reliability. They may build data pipelines to automate the ingestion of data or build entire data lakes.
Data scientists specialize in finding patterns in data using advanced statistical tools. They have often been described as “better at statistics than software engineers.” These advanced statistical tools are also referred to machine learning (ML) tools, which are needed to find patterns in large amounts of data that cannot be interpreted with human perception alone.
Machine Learning Engineer
Machine learning engineers focus on building machine learning systems. While data scientists are performing statistical analysis and building models, Machine learning engineers focus on the design, build, and implementation of machine learning systems. The relatively new field of MLOps, or Machine Learning Operations, is within the scope of a machine learning engineer’s responsibilities.
Software engineers specialize in building software applications using a variety of tools. This may be building a website, mobile, or cloud application. Many software engineers specialize in a particular aspect of development and have a preferred coding language. In the context of a data science team, a software engineer may also build application programming interfaces (APIs) and integrate the finished machine learning model with the front-end user application.
Project managers organize the project and guide the teams to integration and developing a solution requested by the client. They may gather requirements and work with the rest of the team members to keep them on task, in-scope, and within budget.
[Read More: Operationalizing Data Science in the Real World]
Benefits of Hiring a Team
While a hiring a team is often a larger investment from both a time spent — on interviews, onboarding, and management — and salary perspective, there are clear benefits to this approach.
One of the greatest benefits to having a data team is the ability to collaborate on projects. When you have just one set of eyes on a problem there is more chance of missing something vitally important. Multiple team members looking at the same problem can provide multiple perspectives and solutions.
A team of data professionals can collaborate on decision support, insights, and knowledge. Also, certain roles may bring different insights to your organization. Working in a data team presents many more opportunities to collaborate on making decisions, sharing insights and knowledge.
Just like every department, data scientists benefit from the diverse skillsets, backgrounds, and the empowering nature of working in a supportive team environment.
When you have a data scientist that performs the responsibilities of an entire team, it is a challenge to specialize in any one area. There are instances when a data scientist joins an organization to do high-level analysis, and then ends up spending their first year simply doing foundational database development — a task much more suited to a specialist.
You have probably heard the phrase, “Jack of all trades, master of none.” This can create problems when specialization in a specific data tool is important. While some companies start off hiring a full-stack data scientist, most data teams end up hiring specialized professionals when the complexity of the problem becomes too great for a single individual to manage efficiently.
Also, while a data scientist should be proficient in a wide range of skillsets, most specialize in the technical aspects of data science. Functional roles, like project management and business analytics, provide more specialized attention in these areas. Hiring a full data science team that can specialize in each area as needed allows all the specialists to do their best work.
Another problem with hiring a one-off consultant or a single data scientist is that you miss the iterative process of refining solutions in a data-driven framework. Over the course of a project, a data science team will often find better ways to complete a task. But how do you organize and iterate on those findings?
A data-driven framework is a system for implementing what you have learned to build better solutions. This helps a company build a better data culture and a framework to use data efficiently.
If you only have a single data professional on your team, utilizing a data driven framework is more challenging. A lone data scientist may have a harder time getting buy-in from stakeholders, presenting findings, or implementing changes based on feedback, as these are all distinctly different skillsets.
Defining a company’s specific needs leads to better defined roles, more successful hiring practices, and overall, a better return on investment.
Your data needs may not require an entire team. If you are dealing with relatively small client base and minimum number of inputs, there may be little you can do with your current data. A full-stack data scientist could help you build the foundation for your data team until you are ready to hire more data professionals.
While that is frequently a tempting option, some organizations are better served by hiring part-time contractors instead.
Start with asking the question: which problems do you need solved?
- Analyzing data and KPIs: You probably need a data analyst
- Data transformation and building data pipelines: You probably need a data engineer
- Data Modeling: You probably need a data scientist
- Building front-end applications: You probably need a software engineer.
[Success Story: Powered by Data]
Hiring a single data scientist is often appealing because its more cost affordable than hiring individual data analysis, data engineers and software engineers. A single experienced data scientist can no doubt solve most machine learning problems, but at what cost? It is often slower and more expensive that going straight to a specialist.
A dedicated data team can provide a greater impact in your organization. While immediate or ad hoc, siloed projects are easily managed by a single person, this fails to capture the larger picture. High level data management and detailed problem-solving benefit from multiple perspectives. Plus, from a skillset perspective, most organizations don’t need more data scientists; they need data analysts and data engineers, software engineers and developers to support them.
The overall value of a data science team can be boiled down to these ten principal areas:
- Discover insights and share knowledge more quickly than a single person
- Provide more effective information and decision support
- Bandwidth to better track performance and progress of company goals
- Generate signals and warn if something goes wrong
- Facilitate global cross-team collaboration and share best practices
- Democratize data and empower people to use it
- Promote data-driven decision making
- Optimize company services and business activities
- Provide competitive advantage through innovation and developing intellectual property
- Contribute solutions that might revolutionize service or generate new business models
Team Maturity Scales Data Impact
Data science projects, just like any other project, start with a vision and raw material: data.
The process of ingesting, cleaning, and organizing the data for greatest impact is quite the undertaking and may not be a task a single data scientist can complete quickly. To get started, they would have to prioritize, making small improvements and changes, which commonly lead to less impactful insights and a smaller return on investment. That isn’t to say incremental changes in data maturity can’t be crucial. However, having a larger frame of reference improves results immensely.
Having a full data team allows each member to specialize in their aspect of the project. A project manager can have the oversight to make sure the project is in scope and on time. A data engineer can ingest data with best practices, ensuring high data quality and governance. Finally, a data scientist can focus on making impactful insights.
While incremental change is necessary and important, large scale data maturity helps drive massively impactful insights for any business. This large-scale improvement needs vision, specialization, collaboration, and a data driven culture. All aspects that are inherent in full data teams.
Finding the right team to solve your data problems can be a challenge. Who do you hire? How many professionals do you hire? How do you build a data team? How do you get the most for your investment?
RevGen offers four key services in relation to data science:
- Data Analysis: Creative and data-driven exploration of data to answer business questions, generate rapid insights and derive value from your data asset.
- Data Science Enablement: Unlock the potential of your existing data ecosystem. Our team of data professionals can take you from vision to value with a strategy, framework, project identification, and roadmap to capitalize on this value.
- Data Science Solutions: Build fully productionalized data science solutions. Includes problem identification, data ingestion, modeling, deployment, and change management to drive impact.
- Data Science as-a-Service: Plug & Play of RevGen experts to bridge any talent or resource gap in your data science solutions.
RevGen Partners can help support your data needs. We can provide single support for a technical problem or a more holistic approach that might help drive unseen insights and return to your business. Schedule time with one of our data science experts to see what approach would work best for your project.
Derek Plemons is a Consultant in RevGen’s Analytics & Insights practice. He specializes in data science, machine learning and big data.
Jesse Henson has a master’s degree in AI and machine learning, and has several years of experience in the data industry. He is passionate about shaping the future of data and AI technologies.