What to Consider When Hiring a Data Science Team
Organizations do not face identical challenges when using data to gain insights to better run their operations. In previous blogs, we have identified possible challenges that organizations face and professionals that can be hired to help overcome these hurdles. We also discussed the roles that those professionals can play in the organization.
When looking to hire a data science team, it is important that all hiring decisions be based on the need to solve practical problems. In this post, we will shift attention to discussing factors that need to be considered when looking to hire a data science team.
In this article, we will discuss what to consider when hiring a data engineer, a data analyst, a business intelligence developer, and a data scientist. Keep in mind that for a successful data-driven organization emphasis must be placed on developing capable teams rather than individuals. A variety of background and experiences bring improved efficiency to the team. Interaction and learning from each other should be promoted within the team to interpret data well and develop the best recommendations. dc Analyst can help you build a team that fits your goals and can help you achieve your vision.
Data Engineer
Data engineers are also referred to as data architects or ETL developers. Their main role is to import different data sources into a single repository. The data engineer is responsible for organizing data that will be relied on by the data science team. When hiring a data engineer, there are specific interpersonal, technical, and work experience qualities you need to consider.
Team Collaboration
The data engineer should be able to work with other team members without unnecessary competition.
Communication Skills
The data engineer will need to identify data that can meet needs of decision makers and understand business rules that need to be applied to the data. This information will be received from business leaders and IT staff. A data engineer needs to be adept at interviewing people to gather the necessary information to make projects efficient.
Real World Experience
The data engineer needs evident knowledge and work experience of data extraction, transformation, and loading. Knowledge of a popular data ETL tool coupled with a technical certification is essential with when hiring a data scientist.
Professional Knowledge
Work experience and a technical certification in relational databases are essential. Every organization is different. Determine the best relational database platform for your organization to decide on which relational database and ETL tool knowledge is required for your engineer.
Basic Engineer Qualifications
Knowledge and a technical certification of Hadoop and NoSQL databases are essential. Within the Hadoop ecosystem, it is important to ensure the data engineer is well versed in data movement tools.
Business Intelligence (BI) Developer
The BI developer is tasked with identifying reporting needs of decision makers. The person in this role is uniquely qualified to translate reports and dashboards to enable generation of reports without IT assistance. We refer to this as self-service reporting. When hiring a BI developer you need to look for the following:
Team Work
Proficient in Business Operations
Analytical Thinking
The BI developer will work with decision makers in identifying their reporting needs. The BI developer should have a good understanding of how analytics is used in decision making.
Good Communication
The BI developer needs good interviewing skills to enable gathering of reporting needs
Data Visualization
The BI developer should be able to design reports and dashboards that effectively communicate data to the entire team.
Working Knowledge of SQL
The BI developer needs a good understanding of SQL to create queries to provide required reports.
Knowledge, working experience, and a technical certification of a BI tool is essential. Commercial and open source tools are available. Thus, you must determine what is best for your organization.
Data Analyst
A data analyst is responsible for statistical analysis of data. When hiring one you need to look for the following:
Training
Training in quantitative techniques at the appropriate level is essential. Depending on the organization training could be required at the bachelor, masters or Ph.D. level.
Communication
The data analyst will be communicating technical information to non-technical people so they should be able to present information in a simple way. They may also need to train others and write reports. Good speaking and writing skills are therefore essential.
Proficient in Business Operation
The analysts should have a basic and advanced data analysis skills depending on your organization’s needs. Knowledge of statistical software such as IBM SPSS, SAS, R, Stata, and Minitab among others. Your organization needs to identify which statistical tool will meet its needs.
Data Scientist
A data scientist can apply advanced tools and techniques to understand patterns that exist in data. When hiring a data scientist you need to look for the following:
Excellent Communication
Data science is a very technical area, so a data scientist should be able to communicate technical results to non-technical business people. Communication skills are critical because data scientists work in collaboration with business people in identifying problems. A clear understanding of the business problem and how data can be used is important.
Creative
A data scientist needs to be creative in identifying data to be used and in handling data inadequacies.
Good Computer Programming Skills
Skills in data science languages such as R and Python are essential. A deep understanding is not necessary, but the data scientist should be able to solve data science tasks.
Adequate Quantitative Skills
A strong background in statistics and machine learning is essential. The data scientist should be able to correctly identify and use models in problem-solving.
Professional Knowledge
Working knowledge of database design and SQL queries is important. This will enable the data scientist to acquire relevant data for their analysis. A basic understanding of Hadoop tools for big data analysis is especially important.
To identify people with relevant skills organizations need to use multiple interviewing approaches. It is easy to identify technical skills with practical sessions but other skills such as communication and creativity may be challenging to find. Use of hypothetical situations can be used to gauge how a candidate would handle a practical situation. A portfolio of their previously completed project should also be factored in when hiring.
The dc Analyst team is always ready to help you build a data science team that makes sense for your organization. Contact us to get started!
