What does it mean to have the sexiest job du jour?
As a data scientist in Silicon Valley, I get asked that (or a similar) question frequently.
The data science field has received a great deal of attention in the past few years. Harvard Business Review called data science the sexiest job of the 21st century, and Forbes sees it as one the best jobs to pursue in 2016.
While these headlines have piqued the interest of many, a large misconception still exists as to what these professionals’ day-to-day activities look like.
3 Skills of a Data Scientist
A data scientist cannot be summed up in one description.
The profession involves a broad range of expertise, causing the best of the field to also be a:
To be a data scientist, you need to think like a researcher. But instead of lab mice, you’re creating experiments with your company’s data, thinking through interpretations of the data, and implementing a solution that creates the most value for your business.
For example, you may be tasked with understanding why sales increased by 10 percent in a given month. Using statistics, you can understand the potential sources of an increase in sales — was it a given promotion or time of the year? Is this a trend seen on an annual basis?
These puzzles are the types of complex business problems you’ll get to work on.
Your job will consist of gathering large volumes of data and using statistical techniques to show trends in the data. The only way you’re going to get comfortable is by getting your hands dirty. You’ll need to familiarize yourself with different statistical distributions and their assumptions, and in some cases, understand how they’re formulated.
As a data scientist, your main product is data — sales numbers, user figures, engagement — which is generated from a tech product, so you’ll need to develop programs that are able to process large volumes of data as quickly as possible and translate the data into actionable insights.
Contrary to what most people think, very few data science jobs will be purely in data, unless you’re working in research. These days, being a data scientist is an “end to end” job, which means you’re tasked with gathering data, modeling and building out apps to display the data. You’ll need to pull data on your own, often from multiple sources and of different types, and you won’t be able to rely on an engineer to retrieve it, so you can work your magic with data analysis, building predictive models, and the like.
If you’re just starting out, try building up your Python skills, since it has great libraries for handling data and implementing out-of-the-box machine learning algorithms.
There’s absolutely no point in writing amazing programs if they don't help your business colleagues make better decisions and help improve their product or service. Data science’s real power is affecting the business in quantifiable ways, usually by identifying and improving upon the business KPIs (key performance indicators).
From there, you can answer questions from your business decision makers framed using these KPIs. The line of questioning usually goes:
- Descriptive questions — Seek to understand what has happened.
Predictive questions — Where can we use data science to predict what will happen?
Prescriptive questions — What will we do next?
As a data scientist, you should always ‘close the loop’ with your stakeholders and review if it meets expectations and maps to ROI.
Final Data Science Skill: Superhero
Specializing in statistics, programming and business sounds like a daunting task. But getting background in all these skills is incredibly rewarding because you can tackle the business questions that are racking the brains of your company’s senior leaders. You get to play the hero, and be creative to answer all of their most pressing questions.
What are our customers going to buy? When are they going to purchase and how often?
What factors cause sales to drop and how can we set up pre-emptive actions to keep sales consistent? What factors are associated with customer retention and development?
These are just some of the questions a data scientist can answer, making these professionals extremely valuable for the company.
Title image by Jeremy Thomas
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