5 Job-Search Tips for PhDs and Social Scientists in 2025

I learned these lessons the hard way! So you don't have to

Photo by Jed Villejo on Unsplash

👋Hey friends, this is Leihua from Tech Valley. I’m an avid writer on AI, Technology, Data Science, Experimentation, Statistics, and Growth. I appreciate your readership — let’s build a learning community together🍀🔥🤝

Also, please do me a favor by moving this email to your primary folder.

The 2024 tech job market is brutal for everyone. After round after round of layoffs, the market is saturated with qualified applicants. 1 job opening may attract 1000+ applications. The competition is sky-high right now. Not sure if the trend continues in 2025.

So, how do you stand out if you’re looking for new opportunities? The following tips are widely applicable, but especially relevant for my fellow Social Scientists and PhDs. Grad schools fail to teach us how to secure a tech job. I struggled a lot initially and learned all of these lessons the hard way. Hope this post makes your job-searching process more smoother.

Personal Story

I’m a Data Scientist by day but a SS by heart. As a PhD student in Political Science, I especially enjoyed reading papers and exploring various research topics on politics, institutions, and societies. This was the most fun part of being a graduate student.

However, my SS background was perceived as a weakness rather than an asset when I started my job search. Hiring managers assumed I couldn’t code or write SQL fast enough (true story).

😔Sign. There are many more fun stories that I’ll share later.

In retrospect, the entire process was a steep learning curve, and I learned a lot about effective communication, expectation management, building alignment, and other soft skills.

This post is my “I wish someone had told me earlier.”

Job Search Lessons

Mistake 1: ❌ Believing in theoretical innovations > practical use cases.

Here’s why this is a big mistake: 100+ new ML algorithms are developed each year, but how many actually get adopted? The answer is, not many.

Why?

It comes down to tradeoffs. In a production environment, if the existing solution works well, there’s little incentive to rewrite the entire system for marginal gains. Doing so may increase the engineering complexity and overhead, while the benefit is unclear.

Many PhDs, and especially SS, focused too much on the theory and neglect the practical aspects of their research. When HMs evaluate candidates’ future success in the organization, they rely on a guided hunch and interpret the vibe you’re giving off.

It’s somewhat subjective but true.

Sorry to break your heart: if your fancy models don’t have practical applications, HMs won’t find them valuable.

✅ Coping Strategy: identify - pivot - join

If you are an SS or PhD, you’re incredibly talented; If you are a PhD in SS, you’re invincible! You will get your tech job offer soon.

As a start, talking to as many tech insiders and industry veterans as possible. Asking for 15 - 20 min Informational Interviews is a common practice. The goal is to understand their daily routines, essential/preferred skills, etc.

Talking to insiders is the first step to spotting practical applications. If you can’t find any applications, or the use case isn’t significant enough, pivot early and fast.

I pivoted several times. The key is to explore effectively and gauge whether your background fits the area. For SS, consumer behaviors and Social Science questions often overlap. You’ll find it easier to understand and add value to how users behave in specific contexts. From my experience, consumer-facing teams benefit greatly from having SS on the team.

Another trick: join a community and learn from others’ mistakes and insights. Tech Valley serves those who dare to try and fail forward. Join the community now.

Mistake 2: ❌ Reactive > proactive

We all started with 0 network and built it from scratch. The key is to be proactive and start building your network as early as possible. There is a saying:

Your network is your net worth.

Networking is essential to landing a job. All of my interviews come from strong referrals, like someone on the team who knew the HM. Without these connections, it’s unlikely your resume would be picked up for an interview.

Sharing a few personal networking tricks. I sent cold-emails to other data scientists (or any industry role you want). It worked initially, but eventually, no one responded after luck ran out. Then, I connected with friends/alumni/acquaintances on LinkedIn, which had a higher rate of success.

However, building a network needs to be intentional and specific to an area.

Tried a bunch of other approaches with no success.

✅ Coping Strategy: be proactive and create content online

I chose to be proactive.

I started reading extensively and creating technical content online across multiple platforms — LinkedIn, Medium, Red (小红书), and YouTube. Creating content helped establish me as a domain expert in Online Experimentation and directly connected me with HMs who are in the same field.

Very smart move.

First rule of networking is:

Not what others can do for you, but what you can do for others.

Adapted from John F. Kennedy’s inaugural address “Ask not what your country can do for you – ask what you can do for your country.”

If you are a domain expert, companies facing similar challenges may reach out to you for advice. I went from no callbacks to receiving too many interviews to coordinate.

Highly recommended this approach to other SS and PhDs.

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Mistake 3: ❌ Centering your approach around perfection

Perfection sounds appealing but comes at costs. In practice, we can’t wait indefinitely for a perfect solution. For instance, we can't afford to wait a year for a flawless fraud detection algorithm when a quicker solution is needed.

Many SS and PhDs are perfectionists, and it’s hard for them to accept working solutions that aren’t perfect.

I used to be one of them.

✅Coping Strategy: build an MVP and iterate fast.

Once, a very successful UX researcher (with a PhD in SS) shared this tip:

In the industry, a B+ is often preferred.

A or A+ solutions typically have higher engineering and maintenance costs, but the benefit is only marginally better than B+ solutions. Make it clear you understand the tradeoffs and provide justification in interviews.

Mistake 4: ❌ Being too academic makes you nerdy

There is a big difference between being technical and nerdy. Companies want to hire smart technical folks, not nerds. The line between being technical and nerdy is a bit blurred, making it hard to differentiate in interviews.

Again, it’s about perception.

✅Coping Strategy: show me the money!

My approach is to showcase my understanding of the business value and customer pain points before, during, and after the interviews.

Mistake 5: ❌ lack of a framework in interviews

There are two steps to landing a job: getting interviews and nailing them. If you’re actively interviewing, you’re doing well on the first step — keep it up.

However, if you’re getting interviews but no offers, the issue may be in the second step.

Interviewing is a huge topic and deserves a separate post.

Stay tuned until next time.

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