9 Steps to Prepare for AI & Data Science Interviews in Pharma & MedTech

1. Understand the arena you’re stepping into

Before you practice answers, get clear on the landscape. “AI in healthcare” is broad. Hiring managers notice quickly who has done this homework.

Pharma

Focus: clinical trials, real-world evidence, pharmacovigilance, commercial analytics.

Expect conversations around:

Handling clinical data
Working with statisticians and medical monitors
Understanding endpoints, patient safety, and timelines

Biotech

Focus: discovery, experimental data, high-dimensional biology, platform science.

Expect questions on:

Modelling in low-data or noisy environments
Translating academic research into robust pipelines
Collaborating with wet-lab teams

MedTech

Focus: devices, diagnostics, SaMD, algorithms embedded in hardware or apps.

Expect emphasis on:

Validation, explainability, and robustness
Real-time data, sensor data, imaging data
Awareness of regulatory guidance (FDA, EMA, MDR) for AI-enabled products

Take 20 minutes before each interview to map:

“Where does this company sit on that spectrum? Where does this role plug into their pipeline?”

Your answers will feel sharper because they’re anchored in their reality, not a generic “healthcare AI” pitch.

2. Open with a story, not a stack

A lot of candidates start like this: “I’ve been working with Python for five years, I use TensorFlow, PyTorch…”. But hiring managers hear that ten times a week. A stronger opening sounds more like:

“My work sits at the intersection of machine learning and patient impact. In my last role, that meant building models that helped clinicians prioritise high-risk cases faster…”

You still mention tools and methods, but as supporting details, not the headline. That’s how you immediately stand out in interviews for data roles in pharma and medtech.

Use a simple frame for your intro:

Purpose – why healthcare/life sciences?

Focus – what kind of problems you love solving (e.g., clinical prediction, imaging, workflow optimisation)

Proof – one concrete example that backs it up

Keep it tight, human, and specific.

3. Talk about data like someone who has actually seen it

In this industry, data is rarely clean or consistent. It’s fragmented, biased, and wrapped in regulations. When they ask you about your projects, don’t just describe the model. Walk through the data reality:

Where it came from (EHR, lab systems, wearables, clinical trials)

What was wrong with it (missingness, shift, noise, imbalance)

What you actually did (pre-processing, feature engineering, harmonisation)

How you validated your choices

This mirrors how strong candidates talk in interviews at companies like Pfizer, Verily, and other life sciences players, where the role of data scientists is deeply tied to clinical and operational complexity.

4. Show that you understand regulation and risk (even if you’re not an expert yet)

You’re not expected to recite every FDA guidance. But you are expected to understand that AI and ML in pharma and MedTech operate under extra scrutiny.

Demonstrate awareness of:

Data privacy & governance: how you think about PHI, consent, de-identification

Validation: why an internal CV isn’t enough for models that influence patient care

Algorithmic bias: examples of where bias could appear in clinical or device data

SaMD / AI in devices – the idea that an algorithm itself can be regulated as a medical device, with requirements for performance monitoring and change control

Use phrases like:

“We treated every modelling decision as something that might eventually need to be explained to a regulator or clinical lead.”

“We didn’t just optimise AUC; we checked subgroup performance because fairness and safety mattered as much as raw metrics.”

These details signal that you are ready to work in an environment where models influence real-world healthcare decisions.

5. Treat live challenges as collaboration, not an exam

For AI and data scientist roles in pharma, biotech, and MedTech, many interviews now include:

Designing a model

Walking through a real-world clinical or device scenario

Prioritising features or outlining risks

The trick: they’re not just checking whether you know the “right” algorithm. They’re watching how you reason and collaborate. When you get a case like designing a model to flag patients at risk of adverse events during a trial.

Show your thinking out loud:

Clarify the context (phase, data available, constraints).

Identify the target, time horizon, and success criteria.

Flag data quality and bias risks early.

Propose a baseline solution, then an improved one.

Talk about validation, monitoring, and who you’d partner with internally.

In a regulated domain, a candidate who can explain why they wouldn’t use a certain method can be more impressive than someone who tries to force a fancy deep learning solution into every setting. Great candidates narrate their thinking rather than trying to impress with complexity.

6. Make “translation” your standout skill

Technical depth will get you shortlisted, but your ability to shift between technical, scientific, and executive communication is a major hiring signal. Life sciences companies repeatedly highlight the need for people who can sit between data, domain science, and delivery.

Prepare one or two projects that you can explain:

To an engineer: architecture, metrics, scalability

To a scientist: assumptions, hypotheses, outcomes

To a non-technical leader: value, risk, practicality

This signals strong communication instincts and shows you’ve lived in cross-functional environments, and this clear translation is often what moves you from “strong candidate” to “ideal hire.

7. Prove you evolve with the field

AI in healthcare changes quickly. Regulators are updating frameworks, companies are experimenting with new tooling, and expectations of what “good” looks like keep rising.

Expect questions like:

“How do you stay up to date?”

“What did you learn recently that changed your approach?”

“Tell me about a time you realised you were wrong and adjusted course.”

Strong answers:

reference specific papers, guidance, or conferences

show humility (you noticed a gap, you fixed it)

connect new learning back to patient safety or product impact

For example:

“I used to prioritise model performance above all. After digging into recent discussions on AI safety in medical devices and reading about how regulators view adaptive AI, I pay far more attention to monitoring and change management over time.”

That’s the kind of reflection that signals you’re not just chasing hype, you’re building a sustainable career in this space.

8. Ask questions that sound like you’re already on the team

The questions you ask at the end are just as important as the ones you answer. Instead of the classics (“What’s the culture like?”), try questions that make you sound like a future colleague:

“How does the AI/data team collaborate with regulatory, quality, and clinical stakeholders?”

“What’s the current level of data maturity, and where do you want it to be in 18 months?”

“Which decisions today are still made without good data, and how could this role help change that?”

“Where is the company in its broader data or AI maturity journey?”

You’re signalling that you’re already thinking about what a career at their company could look like, and help you evaluate the environment you could work in one day. Shape

9. A simple pre-interview checklist

To make this practical, here’s a quick run-through that you should be able to say before the interview is taking place:

My narrative is clear:
I know why I want to work in life sciences
I can articulate my impact
I have two strong project examples ready

My communication is strong:
I can explain my work at multiple levels

My preparation is grounded:
I understand the company’s focus (pharma/biotech/medtech)
I know how this role fits their scientific or product pipeline
I have thoughtful questions prepared

What role does Panda Play?

Breaking into or advancing within AI and data roles in life sciences isn’t just about preparing for an interview. It’s about understanding the landscape you’re entering: the team structure behind the role, the maturity of the data environment, and the real priorities of the people across the table.

That’s where Panda comes in.

Our consultants help candidates refine their story, navigate technical expectations, and understand how their experience fits into a company’s pipeline or product roadmap.

If you’re exploring your next move or preparing for an upcoming interview, we’ll help you walk in informed, aligned, and ready to deliver impact from day one.

Exploring new opportunities? Just contact our team here to get tailored guidance for your next career step.