want to make an impact Lilly. Data Scientist? This fresh interview experience in September may help you. Our Programhelp students have just finished a round of technical interviews, and we have reviewed the points and difficulties of the whole process, and organized them into this article to share with you.
Interview Process Review
This round was mainly technical, almost an hour long, and was very comprehensive in its focus:
- Python Programming (coding + data processing)
- SQL (aggregation, subqueries, window functions)
- Machine learning (model selection, feature engineering, overfitting treatment)
- Data Analysis & Business Scenario Questions
The technical side of the trainees this time was very hardcore, covering almost clinical trial design, precision medicine, dose-response, machine learning, RWE, basket trial, pharmacovigilance, recruitment prediction, pharmacogenomics and other core areas. Here is the full title .
1. Clinical Trial Statistical Analysis Plan
Design a statistical analysis plan for a Phase III clinical trial comparing a new diabetes drug to placebo. The primary endpoint is HbA1c reduction after The primary endpoint is HbA1c reduction after 24 weeks.
2. Biomarker Subgroup Analysis
How would you analyze biomarker data to identify patient subgroups that respond better to a cancer immunotherapy?
3. Dose-Response in Phase I Oncology Trial
Analyze the dose-response relationship for a new oncology drug using data from a Phase I dose escalation study.
4. Predicting Drug-Drug Interactions
Design a machine learning model to predict drug-drug interactions using molecular descriptors and clinical data.
5. Real-World Evidence Study
Analyze real-world evidence data to assess the effectiveness of a diabetes medication in routine clinical practice.
6. Basket Trial Design
How would you design and analyze a basket trial for a targeted therapy across multiple cancer types?
7. Pharmacovigilance Safety Signal Detection
Analyze pharmacovigilance data to detect safety signals for a marketed drug using disproportionality analysis.
8. Clinical Trial Recruitment Prediction
Design a predictive model for patient recruitment in clinical trials using historical trial data and site characteristics.
9. Pharmacogenomics Analysis
Analyze genomic data from a pharmacogenomics study to identify genetic variants associated with drug response.
Analysis of problem-solving ideas
| module (in software) | Key Points for Solving Problems |
|---|---|
| Clinical trial related | Concerns about trial design, sample size, missing data, multiplicity control, must meet standard statistical specifications. |
| Precision medicine / biomarker | Tie EDA, ML, validation, and biological pathway together instead of just running models. |
| Dose-response & Phase I | Grasp dose-toxicity, safety definition, and PK-PD linkage analysis. |
| ML modeling (DDI / recruitment) | Examine feature engineering, model selection, validation, interpretation, with special attention to avoiding data leakage. |
| RWE Analysis | Familiarity with causal inference tools such as propensity score, IV, and marginal structural models. |
| Basket trial / Adaptive design | Master Bayesian hierarchical modeling and interim futility analysis. |
| Pharmacovigilance | Apply disproportionality measures such as PRR, ROR, IC, etc., taking into account confounding and regulatory reporting. |
| Pharmacogenomics | Focus on QC, association, multiple testing, rare variant analysis, and ultimately land on clinical translation. |
The most important feature of these topics is that they cover a wide range of topics, from clinical trial design and biostatistics to RWE, biomarker, and pharmacogenomics, to machine learning and predictive modeling, which covers almost the entire chain of skills in the pharmaceutical industry. DS's full chain of skills. It is different from the common algorithmic questions on the Internet, and emphasizes more on application scenarios, such as basket trial design, safety signal detection, patient recruitment, etc. In addition to statistical modeling, it also requires an understanding of clinical design logic, regulatory requirements (FDA/ICH), and the answer must be structured to expand from design, methodology, validation to the landing system, rather than a sentence.
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