this Lily. The Data Scientist interview was really the most "cross-border" interview I've ever experienced. The questions ranged from clinical trial design to health economics, AI compliance, and small-sample modeling, and each question was like a mini project in a subfield.
Luckily, I had done a lot of homework in advance, and I had Programhelp's remote voice assistance on throughout the interview, so I was quickly reminded of any snags, and the pace didn't get messed up at all. Here's a detailed review of each question
I. Experimental design for digital therapy
The interviewer asked:
"We're making a digital therapy app for type 2 diabetes management, how would you design an experiment to evaluate its effectiveness compared to standard therapy?"
My first thought was - this is not a traditional clinical trial, you have to take into account the app's usage behavior, user compliance and other digital interventions.
My mind was a bit scrambled, when Programhelp's voice prompts reminded me directly, "Start with the primary endpoint first!"
I immediately settled into a steady rhythm and said:
"First of all, we can't just look at HbA1c reduction, I would add some patient engagement metrics like days active, module completion rate, that kind of thing, to measure whether the app is actually being used or not."
"And then in terms of experimental design, I might consider a stepped-wedge design, both because of the ethical (everyone gets the intervention eventually) and because of the potential for network effects with digital products."
"And I think real-world evidence is particularly important - after all, there can be a big gap between the way apps are used in the lab and in real life."
Here comes the chase:
"And what if the user uses other diabetes apps during the experiment? How do you handle that?"
At this point my mind was spinning as to whether to use ITT or PP analysis, and the voice assistant simply said, "You can speak IV approach!"
I went along with it:
"We can use randomization as an instrument to do instrumental variable approach to estimate the true treatment effect."
"It's also possible to do a dual analysis of intention-to-treat and per-protocol, looking at the strategy vs. the people who actually use it, respectively."
The interviewer nodded frequently.
II. Pricing of Alzheimer's drugs
The interviewer suddenly asked:
"Our new drug has a 30% improvement in cognitive decline in Alzheimer's patients. How would you model an optimal pricing strategy that balances payer willingness to pay and market entry?"
I thought I was going to talk about ML modeling at first, and was about to say random forest, when the voice reminded me, "Don't use a model! This is a health econ problem, talk about cost-effectiveness first."
I adjusted immediately:
"We'll start by calculating the ICER, which is the incremental cost-effectiveness ratio, to see the cost corresponding to each unit of health improvement."
"Then do the budget impact model to simulate cost pressures from the payer's perspective."
I added the triangular balance sentence that Programhelp taught me:
"At the end of the day it's a trade-off between three goals: patient access (being able to use it), payer affordability (being able to afford it), and company profitability (being able to make money)."
The interviewer continued to ask:
"What do you do about the uncertainty of long-term efficacy?"
I almost just said sensitivity analysis, but the voice prompt let me add Monte Carlo:
"One can do probabilistic sensitivity analysis, such as Monte Carlo simulation. and value of information analysis to see if there is a need to collect further long-term data. "
III. Sample less disease modeling
The interviewer didn't ask this question directly enough to understand it, it probably means - we have very little data right now, it's a rare disease scenario, how do you model it?
My head was empty for a moment, and luckily the voice prompt dropped the keyword straight away:
- "The focal loss function can be used to solve the category imbalance problem."
- "Consider also generating synthetic samples with GAN."
- "More importantly, it's really a matter of few-shot learning."
I then expanded on these three points to talk about the implementation and managed to hold up the question.
IV. AI compliance issues
The interviewer asked:
"The FDA is developing regulatory guidance for AI/ML in drug development. How would you ensure that our models are both compliant and interpretable?"
SHAP was the only thing that came to my mind, but Programhelp reminded me to answer in terms of regulations, interpretations, and validation mechanisms.
So I'll start:
"In terms of regulations, I would look to the FDA's SaMD (Software as Medical Device) guidance document, and the upcoming AI/ML model guidance."
Then add:
"Model interpretive aspects can be used with SHAP to look at global feature contributions, LIME to look at local explanations, and counterfactual explanations to provide intuitive understanding."
"In case of deep learning models, attention mechanisms or gradient-based methods can also be used."
Finally on validation:
"Model validation has to have robust testing (different populations), but also detect and mitigate bias, as well as continuous monitoring mechanisms after go-live."
Here comes the chase:
"And how would you write the submission documentation for the model?"
At this point I was almost tempted to say, "We don't usually write documents this complex," when the voice over immediately said, "Model development lifecycle!"
I'll be right back:
"I will prepare complete development lifecycle documentation, including data provenance, model architecture design decisions, validation results, and a complete risk management plan."
Lily's interview was an interdisciplinary battle!
This interview made me realize that a DS in the healthcare direction does not just make models, but also has to be able to design experiments, understand policy, do economic accounting, and talk about patient experience.
It is highly recommended that students who are preparing for biotech, digital health, or healthcare tech positions seriously prep for these types of topics.
This time, I was able to catch the follow-up questions in multiple moments without losing my logic, but I really relied on Programhelp's voice assistance service - whenever I was stuck in my head or my thoughts were scattered, it could help me stabilize the structure and give me keywords with a single sentence, effectively reminding me how to answer and how to develop my answers, without any traces, and with a speed that was just right! The speed of speech is also just right.
If you're rushing for a similar position, leave a comment to share prep! Programhelp The mock interviews and remote assisting services are really strongly recommended.
