There is no doubt that data scientists and machine learning specialists are among the most sought-after experts. As companies worldwide realize that data can be a stark competitive advantage, the demand for seasoned data scientists is only going up.
Companies embrace data in different ways. Some are just starting. Others have already set up full-fledged teams of data specialists. No matter what level of data maturity your company is at, the question of structuring the data team will inevitably raise.
Organizing data within a company can be problematic. Merely hiring a data scientist to make sense of data is not enough. Data science teams require continuous cross-border communication to build data pipelines, create algorithms, and consider all aspects that might not be visible without business acumen. Take a look at the critical roles on data science teams that are vital to bringing your data effort to fruition.
A data engineer is responsible for building, testing, and maintaining data architecture. These specialists lay the foundation, enabling data scientists to glean insights from data. Data architecture prepared by a data engineer serves a the basis for further usage of data.
Data scientists’ job is to analyze data to build prediction algorithms using statistical methods and algorithms. They prepare, explore, and visualize data and build models using programming languages like Python or R.
The Machine Learning Engineer is responsible for deploying and maintaining the algorithms developed by data scientists. They usually work jointly with data scientists.
With a wide range of tools at their disposal, developers ensure a deployed model is delivered to the end-use by embedding it into an app.
According to the Oracle AI article, there is no universal recipe for structuring a data science team. The approach to data varies from company to company, so do the goals for data science in each organization. The Oracle suggests centralized, decentralized, and mixed data science teams.
If your organization has a few divisions or departments like Sales, Marketing, Human Resources or Finances, the decentralized model is the best fit. The same goes if you run several product teams like chatbots, recommendations, virtual assistance and so on. In this model, each department within an organization will have their own data science team. A decentralized data science team needs a dedicated budget, but most importantly, there should be well-defined use cases in place where data science can be applied. The downside of this model is that the data scientists will have little chance to collaborate and share knowledge across the teams.
As opposed to the decentralized model, data scientists here are a part of a team that reports to the manager of the data science team. Those teams can be a solo team as well as ones with hundreds of data scientists on board. The centralized model brings a lot of value thanks to its flexibility. Data scientists can be assigned to different projects, but they are more likely to face the analytical challenges than the decentralized teams. This model also ensures that the best data science practices unified are implemented across an organization. A downside of the decentralized model can be the volume of incoming data science challenges as they all have different demands. When a team supports several projects at the same time, it can be problematic to prioritize and handle ad-hoc and long-term requests alike with the same level of efficiency.
This model combines the best of the two approaches described earlier. Within an organization can be several data science teams. Some of them will be supporting different business functions, while others will be a go-to team when it comes to collecting, implementing, and promoting the best data science practices. A working model is also one where a team belonging to a specific business division reports to an organization’s data science center.
An alternate model of organizing a data science team would be setting up a virtual data science team, operating in a cost-effective location, for example, Eastern Europe. Since it can be problematic to find such rare talent as a data scientist in your vicinity, many companies opt in for remote data excellence centers. That is we at NCube can do for your business. We will help you set up a team of data scientists, data engineers, machine learning engineers, and analysts in Ukraine. The team will work from our office in Kyiv, jointly with your in-house team. Data scientists from NCube are currently supporting the FRST project, Blockchain analytics and strategy company. We could set up a team of skilled data science specialists and machine learning engineers for your project as well. Let’s talk?