Life In Data - The Tasks Of A Data Scientist

Do you want to work in data? What does this mean? For the last 3 years I was at one of the coolest companies in the data space: LinkedIn! LinkedIn has amazing data assets and they have been at the forefront of data science. I want to share what I see as the three key tasks of a Director of Data Science: insights, tools & innovation

Insights, Insights, Insights

Data means nothing by itself: Data has to bring insights one can act on otherwise data is just pain. Data has to answer questions such as, why do we have more sign-ups today? Why is usage up or down? Think of traditional business intelligence but with agility and quick turnarounds enabled by today’s big data tooling. One example of how to create insights is to measure public reactions to our products: how, and how often, do customers talk about them. Very early on my team and I built a tool called “Voices” that collected public reactions to our product and analyzed them with set metrics in order to improve our products. (See here a talk on what is ‘under the hood’ of of our text mining platform)

But Insights are often nimble and very detailed oriented. A big job of anyone in data is to abstract from a detailed discussion in data to enable non-data-savvy folks to participate. You may want to call this “storytelling”, even though the “story” in this case is not fictional. Data should form the foundation but our focus as data scientist is to derive to a simple action, no matter how much data was used in the process. (See my book “Ask Measure Learn“)

As an example take our vision at LinkedIn is to create economic opportunity (Read about the value of social networks). I was fortunate to spearhead a partnership at LinkedIn with the World Economic Forum, where my team helped them to find insights into the skills marketplace. Using our data we could show that a job title is not a good description of any given job, but rather the collection of skills of the ones who work in this job. Thus, a marketing director means something completely different to Proctor & Gamble versus the oil and gas industry. Once you broken down jobs into skills they are becoming tradable goods. You have a skill and you offer them to an employer. The picture below shows the 100 top talent flows worldwide. Those kind of insights can help governments and universities to understand in what kind of education they need to invest. That are data insights at it’s best. (Read more about LinkedIn’s data and the World Economic Forum here)

Tools

But finding actionable insights is not always easy. There is the well-known example that AltaVista lost all of its market share to Google, not because they did not have the data, but because they did not find the right actionable insight. For many this struggle of Data Scientists might be surprising but the reason is actually simple: Managers who understand the needs of the business might not understand data, While data scientists might not understand business needs. To cross this chasm has been a challenge for many companies.