Dear PhD,

Congratulations. You have successfully defended your PhD dissertation, and it was a defining moment in your life. Your professorial experience and teaching assistant credentials are finally going to pay off. Further, you might have hundreds of citations, and PhDs are sought after because of their subject matter expertise. Well, that is OK. All that hard work and discipline allows you to use your newly earned moniker and seek out additional opportunities, either within the scope of academia or corporate options. Wait, Wait, Wait, not so fast. If you are thinking of just strolling into the industry and immediately begin earning a six-figure salary, think again, my friend.

Being at the crossroads of Academia and Industry, we have come across hundreds of PhDs with good Machine Learning (ML) modeling experience, having worked on several large datasets, and have validated the models to serve a large organization.

For example, suppose you are an SME in Agriculture. In that case, you might need to define water and soil health as paramount for climate change and build machine learning models to develop and drive new and innovative technologies to improve water management and conservation at the farm and monitor the quality of surface water and groundwater resources for biotic and abiotic pollutants.

While being an SME in agriculture or any topic is important, breaking into the industry is not that simple. Building ML models to develop improved technologies focused on using nontraditional water sources, such as (treated wastewater) as a variable, for agricultural irrigation and improving irrigation technologies to provide superior timing, distribution, and cost-effective water and chemicals for optimal growth and services is essential. Furthermore, as it pertains to soil health, the dataset you might introduce might include variables on soil nutrient content, microbial functional activity related to nutrient cycling, methods to remediate degraded soils, methods to monitor and increase soil carbon storage capacity, and methods for monitoring and preventing soil erosion by wind and water.

Do not expect a six-figure salary off the bat.

Let us say the company was just awarded a USDA grant for the opportunity mentioned above. Given that the grant has been awarded, there already is a budget in place. That budget is relative to the experience of the data scientist and has a cost line item for personnel needed to complete this budget on time.

With no industry experience, you will not get that PM lead role, and for an industry CEO to hire you, there are a few things you need to understand. First, you need to prove your grit, and while academia appreciates your diligence, corporations want to understand your grittiness and how you will build value for the organization. Gaining experience of any kind in the corporate world and real-world building models is critical at this juncture. Further deploying those models is what the industry wants from you. You can publish hundreds of articles on high domain authoritative sites, build lavish models with training datasets, and even have those models validated, but if they are not deployed in the real world, you have nothing. All of your citations, which might be meaningful for academia, do not contribute to a company’s bottom line. A naive CEO might identify with your academic work ethic, but those in the ML space want to see you gain experience within the industry. To get your foot in the door, you need to remain 100% egoless and take the presented opportunity.

Take the intern position.

Do not get caught up in salary negotiations if you like the company you applied to and want to work for them. Start immediately by taking an intern position, get your foot in the door, and build those models, show them that you belong and that you want to gain industry experience as fast as possible. It will not matter how many degrees you have earned, models you have built, or publications you have authored.

The industry CEO or HR department looks at many resumes and would instead take a Masters in Computer Science with real-world model building experience than a PhD, who is reluctant to commit to an intern position, especially if he/she does not have any real-world work experience. Interviewers will break down your resume to include your data preparation techniques, data visualization skills, model development and deployment skills, if any, and ask if you have monitored any models, and how most importantly, build business value. Further, the candidate who is reluctant after an offer is made is likely not to be chosen. Usually, the HR team or interviewer will let you know how many applicants applied for the ML role. If you are not sure about this, ask. The bottom line, do not get caught up in negotiations if you do not have industry experience. It will not matter how gloriously you defended your dissertation or how many citations you have.

Bottom Line

Always keep this top of mind. You are being interviewed for your subject matter expertise. You have been selected from a large pool of candidates with similar backgrounds, and you are worthy of an opportunity. Any ambiguity in your interview will result in you not being selected, especially if you do not have corporate experience. Remain egoless, show grit given it is a competitive marketplace and find a way to get your foot in the door. It’s challenging to leverage your academic experience and brilliant work ethic without industry experience. Finally, cherish the interview process. With each interview, find a way to exude your humility as often as possible and follow up with the interviewer. Do not get caught up in asking questions about your salary, type of budget for this position. However, ask about your career trajectory and what that might look like in 6 months or a year after your internship. Employers want to know you will be with them long term, and most will accelerate your career as long as you underpromise and over-deliver.