career by moving to industry. Of course, it makes a difference wheth- er you are doing a PhD, already are a postdoc, or have prior industry expe- rience. Yet, some salient points have emerged. While the low pay, temporary con- tracts, and statistical odds of achiev- ing a sustainable academic career are a great turn-off, the ‘academic dream’ is alive with many who emphasize the freedom, passion, and personal satis- faction that comes with research. From the workshop, I have data suggesting that most earn less than €40,000 per year in academia, while I consider it reasonable to ask for €60,000 or more as a data scientist with a PhD. A more critical take is that the struc- ture of academia demands a narrow- ing specialization that increases per- sonal risk (e.g. of later unemployment) while a move to industry enables the building up of a broader portfolio of projects, methods, and technical skills. I support individuals in this transition and see that significant career pro- gress has been achieved after two to three years, e.g. moving on to more senior roles. Most PhD students and postdocs as- sociate a move to industry – quite generically – with more security, sta- bility, and income. Interestingly, many also expect a broadening of opportu- nities by achieving real-world impact through data science product devel- opment. And yes, often there is an interface to e.g. customer experience, business revenue, research and devel- opment. As the workshop facilitator, I can offer additional observations that address the difference between academia and industry: • A significant difference is that ear- ly-career researchers often work individually with longer time lines to publish their results, whereas in- dustry teams work in shorter cycles, possibly with daily deployment rou- tines. • Perhaps the biggest challenge when leaving academia after many years is to get one’s head around the no- tion of the use of business cases. Not peer recognition but rather user feedback and paying customers matter. • Industry and startups expect a pro- 26 | NEUROMAG | July 2018 duction-ready data scientist, so set- ting aside some time for a data sci- ence project with a demonstrated and reproducible outcome is valuable for landing the desired first position. To empower PhD students and post- docs in their move to data science and close any gaps, I have compiled a sim- ple roadmap. I suggest organizing the transition in four steps: 1) Exploration of the field: First, get a feel for and understanding of the role of the data scientist by utilizing e.g. courses, hackathons, meetups, and by interviewing practicing data scientists and credible recruiters. For you, this should result in a stop/go decision. 2) Domain orientation: Which field or industries are you considering? Are you more interested in computer vi- sion, big data or natural language un- derstanding? For health, finance, or automotive industries? As you consid- er your options, look for indicators as to how high or low the entry barrier is. For example: How new is the product? How large is the industry? Are start- ups hiring aggressively and must the wider industry follow suit (e.g. autono- mous driving)? 3) Further training (if any): Your inter- actions will give you an idea if further training is required for a successful transition. If in doubt, you can also in- teract with training providers (e.g. data science boot camps) to see what gets their graduates hired in the domain you are interested in. 4) Career entry: I reckon you want the learning curve to be steep, so a team with a good track record in a vibrant urban environment may be the first choice. A good track record is indicated by a growing team, team members staying for at least two years or more, a product on the market, and growing revenues. There is a way that you can make your move to data science much more fo- cused: By working with an industry- relevant CV from the start. This means writing a new, second CV that you take with you for your interactions and conversations to collect feedback. Your interlocutors and respondents may much more easily have some of the following for you: 1) Suggestions of which domains might be interesting and accessible for you. 2) Network contacts that may be in- terested in your CV. 3) A good idea of what the gap (if any) is vis-à-vis your preferred job, and how to close it efficiently. What do I mean by industry-relevant CV? A presentation tailored to hiring managers and recruiters or human re- sources, making it clear just how you and your skills are relevant. Such a document is always individual. Still, I can offer the following guidance: • The first page should include your mission and search statement, a technical skills overview, and your last employment. • The second page includes your edu- cation, any other employment, and the transferable skills you bring from academia to industry.