Modeling Operations

Clinical Data Scientist – Modeling Operations

About QuantHealth

QuantHealth is a rapidly growing company developing a cutting-edge platform for simulating clinical trials using big clinical data and state-of-the-art AI technologies. Our mission is to provide innovative solutions that help pharmaceutical and biotechnology companies de-risk drug and clinical development, accelerating the delivery of effective therapies worldwide.

We are seeking a 
Clinical Data Scientist – Modeling Operations
 to join our 
Clinical Delivery Science
 team. This team plays a critical role in delivering QuantHealth's AI-powered simulation capabilities to our pharmaceutical partners, helping them make better-informed decisions across the drug development lifecycle.

Role Overview

As a Clinical Data Scientist – Modeling Operations, you will play a key role in QuantHealth's pharma customer delivery projects, leading the training, validation, and adaptation of QuantHealth's clinical simulation models to meet specific project requirements. This hands-on role involves working with real-world clinical data and customizing model configurations, data pipelines, and analytical workflows to ensure accuracy, robustness, and clinical relevance.

You will collaborate closely with cross-functional experts in data science, clinical modeling, and engineering to design and execute modeling workflows that address complex clinical and scientific questions. Working at the intersection of advanced AI modeling and real-world applications, you will help pharmaceutical partners leverage QuantHealth's insights to optimize trial design, predict patient outcomes, and guide strategic decisions.

Key Responsibilities

• Apply and customize QuantHealth's AI-based clinical trial simulation models

across diverse therapeutic areas and drug modalities.
- Lead the model training, adaptation and validation
 process for pharma delivery projects, ensuring both scientific rigor and customer relevance.
- Implement and adjust simulation workflows
 for specific customer use cases.
- Drive project execution, including analyzing simulation outputs
and translating them into actionable clinical insights
 for pharma and biotech clients.
- Conduct exploratory data analysis (EDA)

of 
complex real-world datasets 
to derive meaningful clinical insights.
- Participate in customer-facing discussions
, communicating model assumptions, methods, and implications.
- Collaborate across teams
 - 
AI researchers, clinical scientists, and software engineers to ensure smooth model integration, reproducibility, and performance tracking.
- Contribute to quality assurance, validation, and documentation

of modeling workflows 
to maintain transparency and reliability
.

Requirements

• MSc or PhD in Bioinformatics, Computer Science,
  or another STEM field that bridges biology and computation (BSc with relevant experience will be considered).
• 2+ years of hands-on experience
   in data science or applied analytics within a clinical, biomedical, or life sciences context.
• Strong proficiency in Python and data science stack
  for data analysis and pipeline development (e.g., Pandas, NumPy, Scikit-learn, PySpark).
• Deep understanding of the data science workflow
  , including exploratory data analysis, feature engineering, model training, and model evaluation.
• Experience working with 
  real-world clinical data
   (EHR, claims, registries, genomics, pathology, etc.) and familiarity with their variability and complexity.
• Proven ability to apply data-driven methods in one or more clinical domains, such as bioinformatics, genomics, pharmacology, epidemiology, oncology, or immunology.
• Strong analytical skills and the ability to adapt and troubleshoot models for new datasets and project requirements.

Nice-to-Have Qualities

• Experience with 
  big data framework
  s and distributed computing environments (e.g., Spark, Databricks).
• Familiarity with 
  SQL or R
  for data querying, analysis, and validation.
• Strong foundation in statistics, including survival analysis or clinical outcome modeling.
• Background or domain knowledge in oncology, immunology, cardiometabolic, or other therapeutic areas.
• Experience collaborating with cross-functional teams and 
  communicating technical results
   to external stakeholders.
• A proactive, adaptable mindset with a passion for leveraging AI to improve patient outcomes.