This blog is taken as an excerpt from the Learning Tree eBook, Building Your Data Science Dream Team. Click here to access the full eBook >
The drive to increase confidence in business decisions has never been more important than it is now in today's uncertain digital economy. Most organizations these days aren't struggling with a lack of data, but instead the question of how to transform data into clarity, peace of mind, and valuable business insight. But where to start? As with most large-scale endeavors, it's critical to ensure you have a solid foundation (in this case, the right people with the right skills) before you start building upward and outward.
Identifying the skills and job roles needed to create a data science dream team can be a moving target, but it's necessary to get right. It's not good enough to just know how to process and work with data -- the business must be confident in the conclusions its drawing or else the data cause more harm than good.
Not every data science team will have all roles represented on their team, and the exact skillset needed can vary based on business initiatives and goals. All the same, the roles outlined in the following section can provide a general framework for the players who contribute to an all-star data science dream team.
The main goal of the business analyst is to understand organizational, business, and stakeholder needs and be able to translate and communicate these requirements for the data analyst. The challenge for the business analyst lies in the difficulty in asking the right questions to gain a clear understanding of the business requirements -- a task simpler in theory than in practice.
The business analyst can also be known as business architect.
Relevant Skills: Decision Analysis, Data Visualization, Data Analytics, Communication Skills
The mission of the data analyst is to understand the business requirements obtained by the business analyst and decide what data is needed to address the requirements. Much like the business analyst, the data analyst must ask right questions, so he truly understands the business requirements to collect the appropriate data. Data analysts rely on specialized skills related to visualization tools like Tableau and Power BI. Knowing the best tool for the job based on type and volume of data is under the purview of the data analyst.
Relevant Skills: Decision Analysis, Data Visualization, Data Analytics, Programming (Python or other data science language), Communication Skills
The data engineer is responsible for collecting data through data pipelines, cleaning and transforming the data, and aggregating the data into a database or data source. They must also deliver the data back to the stakeholders. A successful data engineer must determine which tools and programs are best for their data environment. The data engineer is also responsible for ensuring there is a good match between how data is stored and the way it is queried.
Alternative titles for the data engineer include data architect, big data developer, IoT engineer developer, and data designer.
Relevant Skills: Big Data, Programming (SQL, Python, or other big data language), Data Wrangling
Your team's data scientist is responsible for creating models of the system, entering all parameters of the data received, and predicting what will happen given the analysis of the incoming data. It is imperative that the data scientist be able to select the right model for their analysis that will give the most accurate results. The data scientist is highly focused on accuracy, so knowing the risk of any prediction is important, and it is their job to clearly present those risks to stakeholders when necessary.
Relevant Skills: Machine Learning, Artificial Intelligence, Data Visualization, Programming (Python or other data science language), Deep Learning, Statistics, Risk Management, Communication Skills
The data wrangler's goal is to create inputs for data science models based on provided raw data. For those with programming experience like Python and can massage data into the right shape, the data wrangle job role can be a good entry point into a data science career. The data wrangler must know that the data they're using is real, and to know how to extract the relevant information from raw data sources like log files, sensor data, text, and more. Another name for the data wrangler is data science programmer.
Relevant Skills: Machine Learning, Artificial Intelligence, Big Data, Programming (Python, SQL or other data science language)
Your framework administrator is responsible for managing and backing up the data, as well as managing security and access. Understanding data models to be able to perform data mapping is important, as well as the technical capabilities to perform database queries and data analysis. The framework administrator can also be known as database administrator.
Relevant Skills: Big Data, Database Management, Programming Skills (Python, SQL, or other database management language)
The quantitative research is a math and stats person of the data science team with a primary goal of bringing numbers to life through compelling, accurate visualizations. They also have knowledge about the ethics of data science. "What is the cost of an error," is a question the quantitative research will focus on. The quality of information and predictions that are being passed back to stakeholders is their number one priority. The quantitative research can also be known as data visualization design or quantitative analyst.
Relevant Skills: Artificial Intelligence, Machine Learning, Data Analytics, Data Visualization, Decision Analysis, Programming Skills (Python, R, or other data science language)
The machine learning engineer knows how to move from prototype to production as efficiently as possible. They must know the right programs and tools to use for a secure environment that can handle scale, and keep that environment monitored. The ML engineer is responsible for the accuracy of data from step to step as they progress toward the data model to satisfy business needs. Programming skills, experience with clusters, and AI knowledge are all required for success in this role. Alternative titles for the machine learning engineer are enterprise architect and machine learning researcher.
Relevant Skills: Machine Learning Artificial Intelligence, Decision Analysis Data Visualization, Programming Skills (Python, R, or other data science language)
Your cloud specialist utilizes virtual environments to create streamlined operations for the organization and assists in the migration of information and services into the cloud. The cloud specialist must understand and be able to clearly speak on the ROI of migrating the data to the cloud, as well as keep abreast of the constantly changing cloud features within data science and machine learning. The cloud specialist may also be responsible for a portion of managing and monitoring their cloud environment.
Relevant Skills: Machine Learning, Big Data, Artificial Intelligence Data Warehousing
There are many players on the idea data science dream team, and the skills necessary to success are shifting rapidly. The organizations who consistently find success are the ones who most adeptly align their data science strategy to organizational goals and initiatives, and those who invest in their workforce to stay up to speed on critical skills and the tools that up your data science game.
To learn more about how to apply these 6 skills categories in practice, explore the resources below: