A data scientist is a professional who uses mathematical and scientific methods to extract insights from data. Data scientists typically have a strong background in mathematics, statistics, and computer science, and they use this knowledge to develop algorithms that can be used to analyze large data sets. In some cases, data scientists may also be responsible for building or maintaining the infrastructure needed to collect and store data.
They typically work with teams of engineers and business analysts, to ensure that the insights they glean from data are used to inform decision-making. Day to dat, they are responsible for:
- Designing and conducting experiments
- Building and maintaining statistical models
- Developing algorithms to analyze data
- Interpreting results of data analysis
- Communicating findings to stakeholders
As the demand for data-driven insights continues to grow, so does the need for qualified data scientists. However, with the number of available data scientists far outstripping the number of open positions, it can be difficult for employers to find the right candidate. Meanwhile, many data scientists are struggling to find jobs that match their skills and interests. In order to connect these two groups, marketers need to be able to reach data scientists effectively.
The most effective way to reach data scientists is through online channels such as LinkedIn and Twitter. Data scientists are active on these platforms and are always looking for new opportunities. As a result, marketers who can build a solid online presence and engage with data scientists on these channels stand a good chance of making a connection. Additionally, it’s important to ensure that any job postings or other marketing materials are accurate and up-to-date. Data scientists are highly skilled individuals who want to be sure that they’re applying their skills in a challenging and rewarding way. By ensuring that your marketing materials accurately reflect the needs of the role, you’ll be more likely to attract qualified candidates.
An Analysis of Data Scientists
Our audience analytics uncovered six distinct and unique data scientist audiences. This audience was created using bio search with unique self-identifiable key phrases like Data Science, Data Scientist, and #DataScience. The below audience analysis only covers 3 of the six segments (bolded below). Enjoy.
- Data & Analytics
- Journalist/The Media
- Data Science & Machine Learning
- Developer & Programmers
Here’s a breakdown of the unique interests and characteristics of the data scientist audience. This data only represents three of the six clustered audiences from the analysis. We used the audience platform to find 6000 self-identified data scientists. We were able to dissect their follow relationships and conversations to get a better understanding of who they are and what they care about.
It’s interesting to note that each of these three audience segments has very different media preferences in the below analysis. If you think about it, it makes sense that professors or those who work in academia and are also data scientists most likely prefer to read academic media. It’s also interesting to see how several different types of influencers influence this audience. If you think about the real world, it makes complete sense. We have relationships and our circle of influence that we rely on to get information and seek advice.
While it is possible to pull this data manually and run an analysis in excel or tableau, it’s more efficient to use an audience intelligence platform like Audiense.