Nevio Dubbini

Self-employed data analyst.

Postdoctoral fellowships at the University of Pisa, Italy: Laboratory of Clinical Biochemistry and Molecular Biology; and Mathematics Department.

This story comes from our What do research staff do next? project, investigating the careers of research staff who moved from research posts to other occupations and employment sectors. You can use these stories to better understand how these researchers transition, what careers they have and their reflections on the transition process and current career paths.

Nevio Dubbini

Research staff experience

I am an applied mathematician with six years’ research staff experience. I have held two postdoctoral fellowships, in very different sectors.

My first was in the biomedical domain, dealing with the analysis of human brain functional Magnetic Resonance Imaging signals, and specifically, applying and creating new mathematical models of brain activation and connectivity.

My second fellowship related to archaeology. I developed mathematical models to estimate the archaeological potential of urban areas, based on complex/heterogeneous data ranging from archaeological finds and historical sources to geomorphology types. (These data, taken from the different archaeological periods, were properly quantified and elaborated by means of an algorithm implementing a variant of the PageRank model, originally created for ranking web pages.)

I’ve authored about 20 papers, published in peer-reviewed journals and conference proceedings, and I’ve edited a volume within the University of Pisa’s MAPPA project, concerning mathematical methods to predict archaeological potential. Much of my research was carried out within EU-funded research projects. I have been a regular reviewer for two journals, and have operated as ad hoc reviewer of papers for other journals and many peer- reviewed international conferences. 

Transition to a new career

I left higher education research two years ago. Although funding was available to continue my research, I chose self-employment instead. It was a better fit with my interests and values, and I believed it would offer better long-term prospects.

I’ve always liked applying mathematical and statistical methods to real-world problems. I eventually realised the keywords for work like this are ‘data analyst’, or ‘data scientist’, and that’s precisely what I decided to become. Extracting valuable information, making sense of data, and making predictions is what I like most, as well as dealing with concrete problems, which can arise from business or research.

Maybe my decision to leave academia was also influenced by the fact that the strong ‘publish or perish’ trend of research in recent years doesn’t let researchers concentrate on really complex problems. A major part of work in academia is that of writing papers and projects in order to get funding, instead of studying interesting problems. I’m not criticising the system, I’m just saying I realised that I don’t like it too much.

I had to consider carefully the personal risk in going self-employed, and the new things that self-employment brings: the financial side of the work, advertising myself, and so on. Although these don’t take up a huge part of my time, they are significant commitments. I’d got a base to build on, though, thanks to the professional networks I had developed and the extra experiences I had during my research period. As well as attending ‘business’ courses on offer to me, I had been involved in PhD representation activities, which gave me much practice in ‘soft skills’, particularly communication skills. For example, as an active member of Eurodoc (a volunteer, umbrella organisation for 35 national associations representing the interests of early career researchers across Europe) I co-ordinated the work group on researcher mobility issues. 

Luckily, working for myself has gone well since the beginning. 

Current job – and how it compares

I’m now a self-employed data analyst. I offer expertise in design of experiments, statistical analyses of data and mathematical modelling. My customers are private companies or research groups. I help them in turning their data into decisions; querying and explaining data; extracting significant information, making more accurate predictions. Ultimately my service helps in creating value from data. 

I should say most of my technical knowledge is very useful for my work, and I have to keep myself updated continuously. Apart from this, many things are different from the research environment: I have to evaluate every single piece of work in terms of money, pay attention to bureaucratic stuff (contracts, taxation, and so on), and (often) juggle multiple commitments. Sometimes I get contracts that are somewhat boring, but nonetheless I have to fulfil them. In any case, I’m very happy to work in such a dynamic and varied environment.

Competencies old and new

I’ve benefited a lot from my research staff experience in strong interdisciplinary contexts, where a fundamental part of the research activity was related to understanding the needs and issues of the different sectors, and setting a common language. I collected multiple experiences in this interdisciplinary endeavour, spanning disciplines from engineering to archaeology.

Reflecting on my career path

I’m starting to do exactly what I hoped when I left academia. At the moment I’m a ‘sole trader’ but I’m planning to set up a company, probably in analysis of the big data sector. Anyway, these are only ideas for now.

Suggestions and advice

Research is carried on also with the so-called ‘soft skills’. If you don’t need to use these much in your research, try to get experiences alongside your research project where you can further develop these kinds of abilities!