In this blogpost we talk about the different roles and their skills we find in the Data Science world. There is overlap between them and I am sure that you could come up with even more roles, but I guess the ones below give a pretty good overview.
How matter you look at it, “Data Science is a team sport” as DJ Patil stated about a year ago in this article.
The Data Scientist
Roles
- Design experiments: designs and executes the different experiments.
- Get and clean data: retrieving the data from the data sources. Clean up the data sets and make sure they are useful for further processing.
- Analyze data: This is also called exploring the data. This part is important to see early patterns or trends.
- Communicate the results: after the different experiments, the data scientist has to communicate in a simple and clear way the results using different techniques and tools.
Skills
- Statistics (inference)
- Machine learning (supervised and unsupervised algorithms)
- Data analysis
- Data communication
- Software engineering and programming (mostly R or Python)
Spirit
- Willing to find answers
- Not intimidated by new data
- Willing to say I don’t know
The Data Engineer
Roles
- Build data infrastructure: create, setup and configure the different server environments. This can be on-premise or in the cloud (for example Azure).
- Manage data storage and use: keeps an eye on the data sources and makes sure they are kept in good condition and accessible in a performing way.
- Implement production tools: installs and configures the different tools that are used for data science.
Skills
- Hardware knowledge
- Databases
- Data processing at scale (streaming)
- Software engineering
Spirit
- Willing to find answers
- Knows a bit of data science
- Works well under pressure
The Data Manager
Roles
- Builds a data team: The data manager is responsible for the team, he manages the data scientists and data engineers.
- He sets goals and priorities for the team.
- Manages data science process: creating the way the data science process is carried out.
- Interacts with other groups. It is important to communicate with the stakeholders and business people to make sure the questions and answers are clear and unambiguous.
Skills
- Knowledge of software/hardware
- Knowledge of roles
- Knows what can and can’t be achieved
- Strong communication
- Data science
- People Management
Spirit
- Knowledgeable
- Supportive
- Thick skinned
TJA
Data Science on Azure 