The 5-step process for finding data science leader unicorns
Are today’s data science programs consistently telling decision-makers a useful story? Are those departments regularly solving the right problems? When those two questions are not routinely asked, data scientist teams often experience low morale resulting in high turnover. Besides the raw hurt of experiencing high personnel exodus, a company’s leadership that has incurred huge investments of time and resources only to draw a very wrong conclusion or to take no action on vital matters can be devastating. Every decision, even small ones, can impact market share, which rests on creditability, innovation, response time, logistics, design sensitivity and more. Both the public and private sectors need data scientists to corral and query big data in inventive, provocative ways to achieve a competitive edge. But something has gone awry. Data science silos have sprung up across the datascape. What’s the answer?
Organizations seeking the best resources for managing data science programs may need to rethink their tech leader hiring strategies. The industry is young, but a quick look at its short history and a review of the current challenges suggest a useful “unicorn” solution to overcoming the silo problem.
The history of data science and the data scientist position Data scientists of today were the statisticians and mathematicians of the 1980s. The data scientist as an established role took off around the early 2010s with the rise of the internet economy. This role helps firms with recommender systems, fraud detection and churn prediction, for example. The nice-to-have data scientist became a must-have almost overnight in 2012, yet it still applies. The data scientist’s work depends on input from the less glamorous data engineer (who builds the database) and data analyst (also called a business analyst), who gathers insights from the data to make basic assumptions that the data scientist develops. Some data scientists build models for better human decision-making. Others create models for machines to achieve highly targeted outcomes. The third type of data scientist relates to research-based AI and machine learning.
Hiring is a big part of the problem. Most organizations look for a Ph.D. in a related field or with significant experience with machine learning models, and they often drop the management experience requirement to obtain technical talent. This strategy results in hiring a data scientist who does not have the three Cs — culture, context and communication skill sets — to manage effectively. This flawed ethos is cultivated because business leaders want quick answers to stay one step ahead of their competitors. Through no fault of their own, the data scientists respond with solutions that solve no problems or didn’t answer the right question. The cause stems from data science silos that often do not integrate with the overall business. Several organizational charts such as these organizational/reporting permutations are worth experimentation (everyone must continue to iterate) to see which might fit the company’s needs and cultures. However, all the organization charts on the earth will not provide better data science output if the data science leaders’ profiles are not correct. The key to finding effective data science leaders is recruiting for both tech hard skills and business-savvy soft skills. This is best achieved through a hybrid approach to hiring.
A data scientist is not the best person to manage a team of data scientists, which is part of the reason data science programs in many companies are not working as hoped. Percentages vary, yet as few as 10% of all data science programs make it into production. The hybrid/unicorn model involves a mix of technical prowess and business acumen, and a smattering of both senior and junior data scientists. The model includes: Not requiring the technical depth, but rather a basic understanding of data science benefits.
