Preparing for a technical interview can feel like learning a new language, especially when your “native tongue” is academic research. In a recent PhD Pathways virtual session, Dr. Ariana Hackenburg (PhD ’18, Physics; Quantitative Researcher at Google) and Dr. Sarwar Hussain (PhD ’22, Chemical Engineering; Team Leader at Bloomberg) pulled back the curtain on what technical interviews are really evaluating, and how STEM PhDs and postdocs can prepare with confidence.
Below are the biggest takeaways from their candid, practical discussion, plus a few concrete ways to apply them right away.
What hiring teams are actually looking for
Ariana and Sarwar emphasized that technical interviews are not just about getting the “right” answer. They’re about showing you can operate like an industry teammate: think clearly, communicate under pressure, and make progress in ambiguity.
1) Technical skill is table stakes, your thinking is the differentiator
Both speakers highlighted that companies expect a baseline of competence. What separates strong candidates is the ability to:
- Structure a problem (clarify goals, constraints, assumptions)
- Explain your reasoning aloud
- Adjust when new information appears
- Stay calm and collaborative when stuck
Ariana noted that interviewers often want to see whether you can work independently in uncertain situations—because that’s the reality of many industry roles.
2) The interview is also a communication test
PhDs are trained to go deep. Industry teams also need you to go clear.
Ariana shared that one of the biggest transitions she experienced was learning to communicate concisely, especially when updating stakeholders who need the “so what?” more than the full derivation. Sarwar echoed this: strong candidates make it easy for others to follow their logic.
Technical interviews aren’t one-size-fits-all
A key theme was: read the job description like a blueprint. “Technical interview” means different things depending on the role.
- Research-heavy roles may test deep subject expertise, research judgment, and your ability to design approaches.
- Software engineering roles often focus on problem-solving, data structures/algorithms, and sometimes systems thinking.
- Data science roles may include analytics, experimentation, modeling intuition, and communication of tradeoffs.
- Product-facing roles (including PM) may emphasize structured thinking, prioritization, and stakeholder communication—even when the content is technical.
Their advice: don’t prepare generically. Prepare specifically.
The skill PhDs underestimate: translating impact
Ariana and Sarwar repeatedly returned to one core message:
Your research experience is valuable, but only if you can translate it into industry-relevant outcomes.
Ariana encouraged attendees to focus on the “impact story”:
- What problem were you solving?
- Why did it matter?
- What did you build, change, improve, or enable?
- What was the measurable result (speed, accuracy, cost, reliability, adoption)?
Sarwar recommended using the STAR method (Situation, Task, Action, Result) to make your work legible to hiring teams—and to emphasize initiative: how you made decisions, handled uncertainty, and drove progress.
A practical blueprint for preparation
Sarwar shared a concrete preparation arc from his own transition into software engineering:
- Build fundamentals (data structures/algorithms) through a structured resource (books/courses)
- Commit to consistent problem-solving practice
- Do mock interviews to simulate real conditions
- Develop a system-level understanding for engineering roles (how components fit together)
Ariana reinforced that structured programs can be helpful, but regardless of your path, preparation comes down to two things:
1) Practice under realistic constraints
Mock interviews matter because they train you to:
- verbalize your thinking
- manage time
- recover from mistakes
- ask good clarifying questions
2) Build comfort with “not knowing”
One of the most reassuring insights from the session: interviewers don’t always expect a perfect solution. They want to see if you can make progress with guidance.
When faced with a hard or unfamiliar problem, Ariana recommended:
- simplify the problem
- state assumptions
- propose a brute-force baseline
- iterate toward improvements
That approach signals maturity, collaboration, and resilience—traits teams value highly.
The truth about referrals
Referrals came up in the discussion, and both speakers offered a grounded take:
- Referrals can help your resume get seen faster
- But they don’t “carry” you through interviews
Once you’re interviewing, performance is what matters.
Soft skills decide outcomes more often than you think
A standout theme was that team fit and communication can be decisive, especially when many candidates meet the technical bar.
Hiring teams want people who:
- take feedback well
- communicate tradeoffs
- collaborate without ego
- bring clarity to messy problems
- can explain technical work to different audiences
In other words: your ability to be effective on a team is part of the “technical” evaluation.
AI is changing workflows, but not the fundamentals
The panel also addressed how AI tools are reshaping technical roles. Both Ariana and Sarwar agreed: AI can accelerate work, but it doesn’t replace the need for real understanding.
The competitive edge is shifting toward:
- strategic thinking
- knowing what to ask and how to evaluate outputs
- leveraging AI tools responsibly and effectively
- maintaining strong fundamentals so you can catch errors and make good decisions
AI may change how you work, but you’re still accountable for the result.
Closing thoughts
This session offered a clear message for STEM PhDs and postdocs: you already have what it takes, but technical interviewing requires a new form of fluency—one built through structured practice, outcome-focused storytelling, and confident communication.
If you’re preparing for industry roles in data science, software engineering, quantitative research, or product, consider this your roadmap:
- Prepare for the role, not the generic interview
- Translate research into measurable impact
- Practice thinking aloud under pressure
- Use mock interviews to build realism and confidence
- Treat AI as a tool—and keep your fundamentals sharp
About PhD Pathways
PhD Pathways is a monthly speaker series designed to support GSAS students and postdocs in exploring career paths beyond academia. Each session features alumni and professionals who share how PhD training can translate into meaningful, impactful careers across a wide range of sectors.
