Today, businesses rely heavily on data in order to expand their reach and increase their value. Because of this, it is becoming increasingly important for organizations to build data science teams that are up to the challenge of supporting major intelligence-driven ventures. This means that organizations must fill key data scientist roles in order to support processes for data analysis, big data management, and the development of accurate and effective machine learning models.
Keep reading for a high-level overview of some of the tasks and processes for which data science teams are responsible, followed by a deep dive into the various roles that make up winning data science teams.
Data Science Teams and Their Role in Bringing Value to the Business
As mentioned above, we live in an increasingly data-driven world, and this can be highly beneficial from a business perspective. When utilized properly, data empowers organizations to provide functionality and services that increase the value they can deliver to their customers. For example, data-driven solutions are employed by search engines to refine their result sets, which allows end users to get to their desired content more quickly and easily. In the financial industry, data enables banks to develop solutions for detecting instances of fraudulent bank transactions at the earliest possible moment, thereby saving money and protecting sensitive customer information.
With that said, there are several major challenges to leveraging data in this fashion. Both the sheer volume of data and the unclean state that it’s in when it’s collected makes it difficult for organizations to manage and utilize it in an effective manner. This is where data science teams come into play. Data science teams manage, analyze, and operationalize their organization’s data for developing business solutions.
Data Science Teams and Machine Learning
In many cases, operationalizing data for business solutions refers to the process of designing and developing machine learning models. Developing such models is a very complex and involved process that requires expertise in managing large amounts of data for analysis, model training, and model testing. This data must be centralized, cleaned, and made readily available for use throughout the machine learning lifecycle.
Like any modern software development workflow, the process of developing a machine learning solution is an iterative one. Through repeated rounds of data analysis, model implementation, training, and testing, machine learning models are tweaked and refined to ensure that the finished product has high-quality predictive capabilities.
In addition, once a solution is implemented and deployed, data science teams still have a significant responsibility in the realm of maintenance. It’s critical that they continue to monitor and evaluate the effectiveness of their machine learning solutions in production. As time passes, data and model drift will inevitably occur, leading to the deterioration of the effectiveness of the solution in question. When this occurs, data science personnel must be prepared to react quickly to identify the cause of this decreased effectiveness—and the fix often involves model retraining or tweaks to the model code itself.
Data Scientist Roles in Your Data Science Team
Now that we have an understanding of the role that a data science team plays within an organization, let’s take a look at some of the key data scientist roles that must be filled in order to support these processes, as well as some of their key responsibilities.
Data analysts bring value to any data science team. This is typically more of an entry-level position, and it centers on analyzing historical data in order to identify trends and patterns that may be valuable to the business.
Data analysts examine large datasets to glean insights that are impossible to see when simply scanning records with the naked eye. These analytical tasks are often accompanied by the production of various data visualizations (including dashboards and reports) that help the analysts convey their findings to the business.
Another critical data scientist role is that of the data engineer. Data engineers are responsible for managing the large volumes of data that drive data science processes. This is done through the construction and maintenance of data pipelines. These pipelines collect (often messy) data from multiple sources, then centralize and transform it into clean and reliable datasets that will be used in other data science functions (including designing, training, and testing machine learning models).
To perform these tasks effectively, data engineers must be comfortable with defining data models and implementing processes that can efficiently clean and transform large, raw datasets to fit their architectures.
When it comes to applying the complex data science techniques that help power new and exciting data-driven initiatives, data science teams turn to data scientists. Data science professionals are often more seasoned, with the expertise to analyze data, evaluate business requirements, and fully formulate elegant technical solutions to fulfill these requirements. Through their advanced statistical analysis skills, their knowledge of machine learning techniques, and their proficiency with such programming languages as Python, Java, and R, data scientists are able to develop, tune, and produce machine learning models that can be operationalized for use in production applications.
In addition, when machine learning solutions in production decline in accuracy, data scientists can evaluate the model to determine the root cause of the problem. Thus, data scientists are key participants throughout the entire machine learning lifecycle.
In this article, we discussed key data science tasks, including the processes of building and maintaining machine learning solutions. Furthermore, we analyzed the data scientist roles that are critical for carrying out these processes effectively. Data scientist responsibilities include:
- Analyzing data in order to identify trends and patterns that can bring value to the business
- Building and maintaining sizable datasets and associated data pipelines
- Designing, developing, and tuning machine learning models
- Evaluating and maintaining model performance
These tasks are hardly trivial, and they require personnel with the knowledge and skill to overcome the challenges associated with managing and leveraging big data in an efficient and effective manner. By filling the critical data scientist roles of data analysts, data engineers, and data scientists, organizations can build data science teams that are equipped for success in an increasingly data-driven business climate.