Is the grass greener on the other side? As with all things in life, there are pros and cons to moving into industry after being in academia and vice versa. I discuss the differences between the two career paths and things to consider in preparation for the transition. If you are considering moving into a new world of work, this post will help inform your plan and assure you to make the best decisions for your career.

TL;DR

  • Postdoctoral research can be exciting and rewarding but comes with considerable precarity and limited career progression.
  • An increasingly data-intensive world means there is a high demand in industry for analytical minds who approach problem-solving with scientific rigor.
  • Industry roles can provide more stability and a clear career progression plus the potential to earn significantly more than in academia.
  • There are potentially more benefits and incentives to work within a company.
  • Work-life balance tends to improve in industry. However, if you are in an early-stage start-up, you are more likely to have a large workload.
  • Industry roles have their degree of precarity; no job is fully secure. Do your research on companies of interest and assess what your risk appetite is.
  • Universities are focusing on applications of research to data-intensive problems. There are opportunities to gain relevant data science experience.
  • Outreach is a valuable network building tool.
  • Build your network outside of academia, reach out to people in the sectors/companies of interest.
  • Do your homework on the companies you are interested in when applying for positions. You can never be over-prepared in this case.

Potential Challenges in Academia

Having a career in academia can be an enriching experience. To be in the position where you can dedicate your life to the singular pursuit of research ideas and have the opportunity to be at the cutting edge of your field is a wondrous privilege. Despite this, during my time as a physicist, I have seen many of my academic colleagues move into data science or related computer science jobs. This movement is a product of several factors including the composite challenges of sustaining a stable career path in academia. Some have been fortunate enough to continue past postdoctoral fellowships to lecturing in their original domain and beyond, but this is not common. The availability of postdoctoral roles are limited, and the competition is very stiff. To have a deep enough pool of positions to apply to, often candidates would have to scan not only countrywide but internationally.

The geographical spread of opportunities leads to the infamous “multi-body problem,” where you, as a mature person, are likely to have a partner and or a family, making it very difficult to uproot and move to a new city, let alone a new country. Although this does not apply to everyone, I found it to be a regular occurrence and a factor many had to acknowledge in their decisions. If you decide to move, there is no guarantee you will even be at that institution for more than a few years. Most postdoctoral roles, especially junior (immediately after PhD), are fixed contracts usually two years in length. This level of precarity can be further discouragement to stay in academia. There is a possibility of jumping from institution to institution, with little career progression. Alongside these points, some ex-academics simply want a change from what they have likely been doing for many years as well as a new working environment.

Potential Benefits of Working In Industry

There is a mass hunt outside of academia for scientific and analytical minds. In a short space of time, our world has become entirely digitized and technology-driven. Making sense of data and communicating insights are two of the most desired skills in the modern age. Because of the massive demand for data science skills, this role and those related (research scientist, machine learning engineer, data engineer) are highly competitive in terms of salary and benefits. With improved remuneration, you will have the opportunity to tackle diverse and fascinating problems. These opportunities are enticing STEM researchers to look further afield from academic positions.

Data scientists have a broad skill set. They will often rely on inventiveness and having an agile approach to data analysis, making it a stimulating and rewarding role. Working within a company tends to provide steadier career progression and a higher chance of permanency. Many companies offer benefits in addition to salary; for example, some start-ups offer incentivized stock options (ISO), which allow you to own a part of the company. If the company is thriving and has an initial public offering, you will be able to sell the shares, often reaping a handsome profit. In software-centric roles, you will likely have the opportunity to work remotely, which means you could be anywhere in the world doing your work instead of confined to a lab or office. For more information on the roles of data scientist, research scientist and data engineer and the differences between the roles, click through to my article titled “Key Differences Between Data Scientist, Research Scientist, and Machine Learning Engineer Roles“.

Potential Challenges With Working In Industry

As a researcher, you should consider what motivates you. Are you driven by the pursuit and development of your own ideas? Do you thrive in more autonomous environments? It is more difficult to have these aspects of work as a priority in industry, research goals tend to be more narrow (depending on the nature of the company) and on stricter deadlines. If your passion is pure research, unless you manage to find a research-centric role, you will have to shift your focus to providing business solutions, reducing your realm of exploration.

Because your work is tied to the company’s productivity, there may be an increased sense of urgency and pressure that will be more constant than in academia. This increase tends to occur significantly in the market development and market growth stages of the product life-cycle. Academia tends to have shorter bursts of need, usually centred around publication or conference deadlines, with more extended periods of lower demand.

On the other hand, in academia, it is more common to work beyond the typical 9-5 working hours. Work-life balance is trickier to manage as an academic, as I am sure some of you can relate to if you are deep into your degree or research fellowship. In a typical day job, you can leave your work at the office if you choose to, and it is much easier to balance your work with the rest of your life. However, depending on the company you work for, and the stage of development the company is in, you may have a large workload requirement. For example, if a company is an early venture, and the workforce is still growing, you may be required to do parts of several roles at once with extended work hours.

Data scientists have a broad array of skills, therefore they can often be asked to solve any “data-related” problem. Whereas in academia, you are more likely to have a narrow focus, in industry, it is likely you will be asked to do tasks that are outside of the remit of your role. It can be draining to juggle a large number of tasks continuously.

With the aforementioned demand for data scientists, it is very inviting to snap up the best paying and most interesting job out there. But businesses are not immune to precarity. Any business can go under or experience bad times, leading to redundancies. Research institutions are less susceptible to the uncertainty of the world of business, so if you are in a research role, for the duration of that contract, you are unlikely to have to leave because the institution failed.

Both academia and industry have their distinct pressures, patterns of demand levels, and they can vary from institute to institute or company to company. It is up to you to weigh up these factors and decide on what is acceptable for you.

Universities Are Excited About Data Science!

If you are finishing your degree or have already completed it, it is worth considering an academic role specializing in data science. Getting a postdoctoral fellowship or masters in data science will stand you in good stead for being one of the top applicants for data science roles. If you can get additional experience together with academic credentials, you will be in a commanding position.

Universities are creating more data science integrated degrees and research fellowships. Your university likely has a dedicated department for exploring applications of STEM research to “big data problems.” In other words, using the abstract reasoning and data analysis skills that you will have gained during your degree program to solve real-world problems.

Universities are frequently collaborating with private companies who have a wealth of data-intensive issues but may have a deficiency in analytical minds nurtured in Academia. There will be an academic adviser in your department who has some responsibility for applications of STEM to industry. Reach out and share your experience and aspirations with them. People want to help people, and if you are passionate, that will entice people to invest in your journey. There is likely funding dedicated to industrial applications, and all that is needed is an idea or the right person to lead a project; it may be a case of serendipity if you knock on their door.

You should aim to build your network beyond your institution. Researchers can build their networks using LinkedIn to contact scientists, company founders, and talent finders. By being in touch with others active in data science, you can stay up to date on essential conferences or events that are happening, research and developments made, and potential job opportunities.

How To Get Access the Best Opportunities in Industry

You do not need to be in a degree program nor wait until the end of your degree nor do another degree to start forming the next steps in your career path. Outreach presents a fantastic opportunity to communicate your passion and put you in front of people who may be interested in your skills. The more actively you demonstrate your skill set, the more possibilities there are to meet people who can recommend you for internships, further research, and employment. As part of my University’s outreach program, I took part in several Royal Society Summer Science Exhibitions, sharing the particle physics experiments of the Large Hadron Collider. To further increase your visibility, find meet-ups that are focused on data science and machine learning, and attend them.

If you have work that may be of relevance to the meet-up, offer to present at one of the meetings. People will be fascinated by your work, and they will want to network with you, including company owners looking for researchers to join their team. Some internships exist to bridge the gap between science and data science. They typically range from a summer to a year in duration. These are useful for getting hands-on experience with a real-world data problem for a company while increasing your visibility. You also get a taste of what it is like to work in a non-academic environment. If you want to jump straight into industry, look for companies that have a strong track record of hiring previous academics, have data science as a core part of the product, and have a focus on research and scientific rigor. Doing this will ensure you sift out roles that claim to involve data science but are only using the name as a buzz word (similar to AI).

Doing Your Homework

When seeking a job, the same amount of research you put into finding the ideal university for your degree or postdoc is the amount you need to put into finding the right company for you. You must gain a deep understanding of the company that interests you. In addition to the product and the technology itself, the workplace culture, history, competitors, business model, and the general market within which the business resides are all essential. You must also gain as much understanding of what the position entails. You can do this by meeting with people who are in that position and who are in the company. The more you invest at this stage of job-finding, the more assured you will be of the roles you do land and the smoother the interview process will be.

Concluding Remarks

In this post, I have discussed the pros and cons of academia and industry and have highlighted the importance of assessing both career paths. You can take steps to guide yourself into a data science role, and there are many tools out there to help you do it. Investing in your academic career and network will help elevate your skills and your desirability in the job market while providing you with the opportunity of pursuing your research ideas. At any point in your career, you can decide to build the skills necessary to be a data scientist. If you are coming from a STEM background, you already possess the foundations to do so. With online learning, internships, mentoring networks, and camps you have the abundance of resources to choose from and the flexibility to do so at your pace. You can take advantage of the best available courses for becoming a data scientist by clicking on the Online Courses page here. If you are successful in landing a career in data science, visit my blog post which provides tips for early career researched-focused data scientists.

Whatever your path is, I hope you enjoy it and learn as much as you can about it. If you enjoyed this post share it and join the Research Scientist Pod mailing list. If you have further comments or questions feel free to add them to the comment section below.