Beyond these three core vocational skills, several critical soft skills are frequently mentioned in job postings, and can lead to greater success for your data scientists. These are skills that you should be looking for. They include communication, data scientists must communicate effectively up and down the data supply chain. First, to obtain the data that they need. Second, to work with those who understand the business meaning behind the data. Third, to articulate the findings and implications to business leaders in a language they understand. Key components of these communication skills are those of persuasion and expectation management. Only in enterprises with true information centric cultures or information oriented business models, do we find data scientists working in stream with core business units. More frequently, data scientists are in an R&D function expected to run experiments and only surface when they believe in idea warrants implementation. The ability to insert themselves into core business functions and assert their ideas is therefore critical. The next soft skill is collaboration. As data scientists increasingly emerge from laboratories to work directly for business leaders and side-by-side with business unit personnel, they need to shed the introverted statistician stereotype. Increasingly, business professionals require access to analytic techniques beyond basic math, and must be able to rely on individuals such as data scientists to work closely with them. However, teamwork isn't just about becoming a welcome and helpful colleague, it's also about the ability to juggle competing priorities and pressures. Another soft skill sought in data scientists is leadership. In larger well-established advanced analytics team, the role of the data scientist can incorporate management responsibilities. Some organizations even have a chief data scientist. Now, this will include directing the efforts of a team of statisticians, data administration, and integration professionals, the data visualization, reporting, and application integration developers. Beyond team management, data scientists are also assuming a greater role in driving business strategy. Recently, we've seen some top executives retain a personal cadre of analytics professionals, led by a chief data scientist who gets a seat at the senior management table. Most organizations are also looking for a data scientist who is creative. While staff statisticians typically work on assigned algorithmic problems, and BI analysts typically work on designated reporting solutions, the work of the data scientist is very much an innovation oriented exercise in solving open-ended conundrums. Data scientists are tasked with finding opportunities to optimize, expand or transform the business through the lens of information. Moreover, data scientists must be creative in sourcing data, modeling problems, and employing a range of analytic techniques. Data scientists also need discipline. Although creativity is critical, data scientists must remember that science is part of their directive. This means following established scientific methods, employing legitimate techniques, using validated, and embracing causality, not just correlation. Scientific methods demand that questions are well-defined, true data or observations are collected, and hypotheses are formed. Also that investigative methods or selected data is analyzed and interpreted with yielding conclusions, and the results are formally communicated and tested. Although a rigid methodology is certainly recommended, results perfection is not. Opportunity costs in a fast-paced marketplace are too high to spend excessive time in achieving incrementally better analyses. However, a data scientist just as any good statistician or other analytics professional, needs to understand the differences between correlation and causality, and between incidental and insightful patterns. Finally, a great data scientist has passion. Many of the data scientist's job openings that are reviewed feature the obsession for information, solving insurmountable problems and finding unique ways to accelerate the business. This drive is a key characteristic of the individuals that organizations should seek for this role. Telling your data scientist, "Nobody has yet been able to solve this problem, " should more than adequately fuel their passion. Ultimately whether your organization calls the role, data scientist or something else, individuals or teams of professionals with these core skills and soft skills will prove essential in maximizing the realized value of your information assets, and for discovering opportunities for enhanced business performance and competitive advantage.
