Many of us have come to expect that our favorite retailers, personal finance apps, media outlets, and social media platforms will treat us like individuals, predicting our preferences and anticipating our needs. To deliver these personalized experiences – and avoid disruption from startups – large companies have bet big on interpreting customer data.
But most organizations don’t have the technical chops (or time) to analyze that data, let alone use it to build targeted engagement strategies. To fill the gap, companies have started to compete for data science talent.
It’s a battle that’s nearly impossible to win. The number of open data science and advanced analytics positions has increased by 650 percent since 2012 and will grow by an additional 28 percent by 2020. With only 35,000 people in the US with data science, talent can have their pick of companies.
In the scramble to find data science talent, few take the time to answer the necessary questions to set their teams up for success: Would a data scientist serve our needs best, or do we actually need a statistician or analyst? If we do need a data scientist, what could we reasonably expect them to solve for us? And what kinds of interesting problems do data scientists actually want to solve? What are the top companies that made big bets on data science? What was the payoff?
Over the past year, we found answers to these and other questions from experts who cut through the information overload and delivered clear, informed perspectives on the state of data science. Here’s the primer.
We asked experts a simple question: what is data science, and how does it differ from statistics? This is a must-read for anyone hiring for data science roles or pursuing a career in one of the hottest fields in tech.
Lon Riesberg was just trying to keep up. He wanted a place to keep all the articles he was reading to try and remain in the loop on the latest in the always-evolving field of data science. The best of what he was reading became the Data Elixir newsletter and community, and he shared how that drive to learn is the key to sustaining a career in data science.
Every company has distinctive data sets. It’s the data scientist’s role to make them into something useful. At Instacart, Data Scientist Jagannath Putrevu felt his company could be doing a better job getting its groceries from the store to the customer. In order to test his theory, he ran tens of thousands of simulations to see how close they could get to perfection. The results changed the way Instacart operates in hundreds of cities.
Airbnb Data Scientist Alok Gupta left academia for Airbnb, but he didn’t abandon the methods he learned there. Learn why, when it comes to measuring Airbnb’s most valuable asset—trust—Alok teamed with Stanford to do an actual study.
Irzana Golding believes that the most powerful tool at a data scientist’s disposal is whipping out a pencil and paper and applying some critical thinking. So when faced with the challenge of how to measure the effectiveness of Cisco’s chatbots, she had to think outside the box. Find out what it took to accurately assess if their chatbots were giving customers the answers they needed.