Beyond technical proficiency, there are several skills that a data scientist must have to excel.
In their writing, presentations and emails strong data scientists are clear. They focus on the audience, considering what they already know, what they need to know and what they care about. They can explain their methods and results to a non-technical audience at the appropriate level of technical depth.
Data scientists that are lacking in this area fail to convey their work or persuade teammates and leaders of its importance.
Strong data scientists are not afraid to move between roles, say, migrating between data analysis, data engineering, modeling, and back, over the course of a project. This breadth provides a huge benefit. For example, doing data analysis while keeping the limitations of modeling in mind produces results that are more accurate, more useful and more timely.
Data scientists that are lacking in this area might say "I'm a modeler. Data cleaning is a job for someone else." Overspecialization leads to blind spots, such as neglecting code health or neglecting statistical rigor.
Data scientists have to learn new tools (e.g. new languages, new applications, new techniques) with each new position, and sometimes with each new project. There's no practical way to learn all the tools you will need before you need them. The only way to be prepared for this is get comfortable with the process of learning. The set of tools a data scientist comes with doesn't matter as much as their ability to embrace new ones.
Data scientists that are lacking in this area will be limited in what they can contribute. Most project work will be frustrating. (Their teammates will be frustrated too.) The solution is to adopt a willingness to feel dumb, also known as "a beginner's mindset". This helps navigate the uncomfortable start-up period when every step of working with a tool is unfamiliar. The beginner's mindset manifests itself as a curiosity about the field, the company, the products, and the customers.
The strongest data scientists are those who have a broad understanding of all the roles a data scientist can play and have deep skills in at least one. In our experience, these data scientists are the ones who have worked on realistic data science problems in several domains. The skills required to work with data are tough to learn in the abstract. Concrete examples with rich context and ambiguity are powerful teachers. Applying the same skill in several different domains bestows a facility on the learner that is hard to get any other way.
Data scientist that are lacking in this area will be confused by the quirks of real data and overwhelmed with the challenges of using their skills on unfamiliar problems.
There is no better way to develop a deep mastery and rich understanding of the field than to share your work with others. This can take the form of teaching activities with those less experienced, such providing advice, tutorials, or explanations. It can also manifest between peers in such varied ways as publishing project summaries, asking advice, cooperative coding, and creating cheat sheet references for a new tool. These can take place in person or on-line. Every major social network has its own data science community, each with its own flavor.
Apart from technical proficiency, a data scientist with a broad set of skills learned though practice, an eagerness to learn more and the ability to tell others about their work can be a powerful contributor.