Transitioning from a PhD to industry jobs is becoming increasingly common in many disciplines. I am an economist who works in tech alongside fellow PhDs from various fields, including statistics, computer science, neuroscience, physics, biology, and chemistry. When I was still a student, the availability of information about how to get a job in the private sector was limited, and I wish I had more guidance and access to people who had made the transition to ask questions. This article will guide you through the essentials of successfully going from PhD to industry.
Before you get a job: if you can – do an internship
- Extending your PhD for an extra year can be beneficial if you can do an internship.
- An internship helps you understand the importance of soft skills, especially collaborative teamwork, in the tech industry.
- Internship experience also sends strong signals about your teamwork capabilities.
- Many companies can extend a full-time job offer to interns who perform well – this is something you should ask about when you apply for internships.
PhD to industry: getting a job
- Your network is your net worth – you should always be networking, as you can’t build a network overnight.
- If you start reaching out to people on LinkedIn, you may be surprised at how open people are to sharing their experiences and giving tips.
- The National Association for Business Economics (NABE), especially the NABE Tec conference, is excellent for networking if you are an economist or interested in working in a data-science-adjacent field.
- Your network will likely not only help you with your first job but also with your future jobs.
- Networking is also a possible avenue for receiving job referrals.
Advantages of Referrals:
- Increases the visibility of your application to recruiters.
- An existing employee vouches for your suitability as a candidate
- Approach for Referrals:
- Reach out to employees on LinkedIn with a concise message (think three bullet points).
- Highlight why you’re a good fit and attach your resume.
- Offer to hop on a call if they’d like to.
Where to apply
- Job titles in tech are inconsistent; different titles can describe the same thing, for example, Economist, Applied Scientist, Research Scientist, and Data Scientist.
- This is why you must always read the job description.
- Remember that there is randomness in the hiring process, and not getting an interview or an offer does not necessarily mean you’re a bad candidate.
- Sometimes, hiring managers are not 100% sure what they’re looking for, and you may be able to convince them that you are the right person for the job if you are passionate about it, even if you don’t meet all the requirements stated in the job description.
- It may also be hard to tell if you would like the job purely from the job posting; it may be worth applying to jobs that sound less than ideal, but sometimes those turn out to be your dream jobs.
- If nothing else, getting interviews provides an opportunity for interview practice.
- Interviewing is a skill that needs to be practiced; it’s not only about what you say but also how you say it.
- Adopt the STAR method (Situation, Task, Action, Result) to structure your interview responses. This method helps convey not just the content but also the context and impact of your actions.
- Show that you can think about a problem holistically before diving into the details of the solution itself – this is very important and is often overlooked by candidates fresh out of school.
- Always ask clarifying questions before going down a rabbit hole of an answer.
- For more info, read my technical interview guide.
Importance of a good manager in your first job
- A manager can greatly impact your job experience, either positively or negatively.
- Ask questions during the interview to understand the management style and see if it matches your preferences. Remember, it’s not only the company interviewing you; you are also interviewing the company. Asking questions shows maturity and genuine interest.
- Asking about a potential manager’s philosophy can provide important insights. Good managers will appreciate this question and provide a thoughtful answer.
Remote vs. in-person
- It comes down to preferences; nothing says you can’t successfully grow your career in a fully remote environment. Especially coming out of the PhD you are used to leading and structuring your work.
- In-person interactions definitely have their pros, as they can facilitate more spontaneous communication.
- Some companies are fully remote but organize team offsites a few times a year; others have partial (flexible or mandated) in-office days.
- In the end, it boils down to what makes you most happy. The better you network and increase your value as an employee, the more options you will have. I believe that fully remote is here to stay, and those who like this work style and who are good will always be able to find a fully remote job.
Once you get an offer – what to look for in your first job
- Be aware of potential red flags before accepting your first offer:
- Is the problem space you’re going to be working on well-defined?
- Can the hiring manager convey how your position will benefit the business?
- If you have options, prioritize environments where you can gain various skills – think of your first job as trading your time in exchange for learning new skills that you can use in your future career.
- Aim for a role that offers exposure to different teams and projects.
- A well-rounded first job can significantly boost your market value for future roles.
PhD to industry: success on the job
Setting deadlines and defining deliverables
- The ability to accurately estimate time for tasks and adhere to deadlines is an acquired skill that you should start honing as soon as possible. This involves being realistic about how long things take to execute and may require that you provide pushback if someone proposes a timeline that is too short.
Challenges with Working with Data
- Data in academia is often “manicured,” whereas it’s usually messy and unstructured in the industry.
- If the data is very “large,” you may have tradeoffs between the theoretically best estimation method and the feasible one.
- You need to be prepared to justify your modeling method and push back in discussions.
- Be comfortable with uncertainty and open to feedback.
Career and Skill Development:
- The type of learning needed in the workplace often goes beyond traditional educational methods.
- In tech, you need to sell your solution and its usefulness, and to do so, you have to use no technical jargon and relate the solution to a business problem and decision. This is a different mindset than in academia and takes some practice.
- Don’t limit skill development to your background – focus on communication skills, i.e., speaking and writing. Clear writing indicates clear thinking and is rarer than you’d think. Remember that presentations in business are not like academic presentations; aim for no equations as a general rule.
- Remember that almost everyone experiences imposter syndrome.
- Work proactively on making connections with coworkers and set up coffee chats with people outside of your immediate team.
- Be collaborative and help people when you can. This pays off in the long run.
Is a PhD worth it if you want to work in the industry?
- The value of a PhD is context-dependent; if you are looking to work in a specialized technical role, you may need a PhD. For example, roles like causal inference and macro modeling, more often than not, require a PhD
- A PhD serves as a signaling device that shows that not only do you have a certain level of technical knowledge, but, more importantly, you can be trusted with finding solutions to ambiguous problems (aka research)
- Doing a PhD requires sacrificing 5-7 years of time and foregone income – on the flipside, it can be quite a nice lifestyle with a flexible schedule.
- If you are considering a PhD, you probably already have a strong interest in the subject. If you’re on the fence about it, feel free to reach out to brainstorm.
Do you still have questions? Feel free to reach out to me directly.