Think of all the strengths you have to showcase as a data scientist. You may have a background in statistics as you sometimes develop new statistical theories for big data. You can do statistical modeling, experimental design, data reduction, sampling and clustering, testing, modeling, and predictive modeling. Then the different types of data scientists have a solid background in mathematics that helps with analytic business optimization. You have developed some business acumen too as you tackle ROI optimization and are dabbing in decision science. And then again, you can be a data engineer, so you optimize architectures and data flows. You can be in machine learning or production code development. But you are also strong in visualization and are dealing with things such as spatial data and graph databases.
Data Scientists: The Big Three
Data science is not a magic swamp with one singular inhabitant, “the elusive full-stack data scientist”. This is a highly differentiated field that is becoming more and more diversified. There are over 10 different types of data scientists right now. But depending on the taxonomy, there might be even more. Yet the classics remain. These can be captured in the big-three model of data science: the data analyst, the data engineer, and the data scientist.
The Data Analyst
An outstanding analyst is the very prerequisite for the success of your data efforts. A solid background in statistics is what brings rigor to data-driven decision-making. A data analyst looks at industry data to answer business-relevant questions and delivers these answers to the relevant teams. Data analysts transform large data sets, form hypotheses, and communicate these to business decision-makers. So they need to demonstrate a strong sense of the processes taking place beyond the data.
Complex data analyses and insights have to be conveyed clearly and crisply to an audience without prior knowledge of probability and statistics. So the data analyst looks at the data, which involves cleaning and statistical analysis, and then goes on to visualize the data and articulate the results. As a data analyst, you will stick to the facts. This means dealing with the concrete task of answering business questions and gleaning insights from existing data.
The Data Scientist
This may involve anything from data analytics to building machine learning models that predict the future based on past data. Unlike analysts, data scientists do not stick to the facts entirely. They have more space to develop their own ideas or discover patterns in the data that seem worth pursuing. To detect these patterns, you will analyze massive amounts of complex structured and unstructured data. You will make complex assessments. These may involve analyst tasks such as harvesting, transforming, and visualizing the data. But you may also end up building and training a machine learning model. Apart from a solid background in statistics, you may be trained in supervised and unsupervised machine learning methods.
The Data Engineer
This is a software-development-intensive role that thrives on programming skills and the ability to make data tangible to data scientists. Data engineers manage large datasets, do the data cleaning, aggregation, and ETL processes. But they also build data pipelines to get the data to the analysts and scientists within an organization. In this role, you may mostly deal with data acquisition tasks and batch or real-time processing of harvested data. In all likelihood, you will also be responsible for developing, building, testing, and maintaining the infrastructure that allows for storing and accessing data. You also improve data quality and reliability.
The Different Types of Data Scientists: There Is More
This is not the whole picture, however. The Big Three types of data scientists have diversified into multiple specialty roles, some of which are not even considered to belong to the field of data science as such. Let us look at some of them:
- Machine Learning Engineer. Working at the intersection of software engineering and data science, machine learning engineers have mastery of a breadth of software tools and are adept in the delivery of practicable software solutions. A machine learning engineer takes the prototyped (theoretical) model proposed by the data scientist and makes it usable in a production environment. MLEs create programs that control devices and develop algorithms that help machines identify patterns in their data, comprehend commands, and even learn to make their own decisions.
- Machine Learning Scientist. Unlike machine learning engineers who are specialized in building machine learning infrastructures, machine learning scientists focus on researching new approaches and investigating new algorithms. The outputs of a machine learning scientist are reports and whitepapers.
- Statistician. Works in both theoretical and applied statistics with an eye towards business goals. Using mathematical techniques, statisticians analyze, interpret, and report statistical information and draw business-relevant conclusions based on the data.
- Business Intelligence Developer. Using BI tools or creating custom applications for BI analytics, BI developers work on strategies that help business users enhance their decision-making processes.
And Here Come the Architects
- Data Architect. Developing, building, and maintaining a company’s data architecture solution, data architects ensure the high availability of company data. They create databases, prepare structural and installation solutions, and issue design reports.
- (Big Data/Cloud) Infrastructure Architect. Overseeing the company’s big data, cloud computing, or general data strategy, the infrastructure architect translates business requirements into concrete systems applications or process design for IT solutions. The infrastructure architect makes sure that the business systems are working, meet the necessary system requirements, and are able to support new technologies.
- Enterprise Architect. Making sure that a company is using the right technology and systems architecture to successfully implement its business strategies, the enterprise architect shapes the image of an organization’s strategies and processes.
- Applications Architect. This role involves the design and creation of new applications as well as monitoring the behavior of existing applications within an organization. Application architects develop product prototypes, run tests, look at the ways their apps interact with users, and generate app development manuals.
As data analysis methodologies become more powerful and the volumes of harvested data continue to grow to unprecedented levels, the number and diversity of data science roles will continue to expand. Find out more about our available roles and on-the-job training opportunities.
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