QuantHealth is a growing AI startup in the clinical trial space, leveraging AI, biomedical data, knowledge graphs, and real-world patient data to simulate and optimize clinical trials for pharmaceutical companies. Our platform combines large-scale biomedical knowledge, clinical trial data, and patient-level data to help customers simulate clinical trials, reduce development risk and cost, shorten timelines, and improve the probability of clinical trial success.
As a Data Scientist in the CDS R&D team, you will play a key role in researching, developing, and productionizing advanced data science solutions that power QuantHealth’s clinical simulation and prediction capabilities.
This role is designed for a strong applied data scientist who enjoys solving complex, ambiguous problems end-to-end: from data exploration and feature engineering, through model development and validation, to building scalable pipelines and AI-driven workflows. You will work closely with clinical experts, product, delivery, engineering, and other data science teams to translate challenging clinical and biomedical questions into robust data-driven solutions.
The ideal candidate brings several years of hands-on experience in applied data science, machine learning, and data pipeline development. Additional qualities we look at in candidates are scientific thinking, project ownership, and the ability to independently learn and operate in complex domains. Experience with biomedical or clinical data is a strong advantage.
This position involves working with a modern Python-based data science stack, building predictive models, developing data and ML pipelines, and contributing to the continuous improvement of QuantHealth’s clinical trial simulation product. The role requires curiosity, strong self-learning abilities, research orientation, and rigorous analytical thinking.
· Design, develop, and validate data science models, algorithms, and tools that support QuantHealth’s internal and customer-facing clinical simulation products.
· Build end-to-end data science solutions, including data ingestion, feature engineering, model development, evaluation, and deployment-ready pipelines.
· Collaborate with clinical, product, delivery, engineering, and data science teams to prototype and implement data-driven solutions.
· Explore, process, and integrate diverse data sources, including clinical trial data, EHR/RWD, biomedical literature, biomedical ontologies, and knowledge graphs.
· Develop scalable ETL and data pipelines to support automated clinical simulation workflows.
· Build LLM-based and agentic systems to automate internal research, data processing, and biomedical reasoning workflows.
· Develop and improve data projects using tools such as PySpark, PyTorch, PyGeometric, MLflow, SQL, and the broader Python data stack.
· Stay current with emerging methodologies in machine learning, LLMs, knowledge graphs, biomedical AI, and applied data science.
· Contribute to internal knowledge sharing, best practices, and scientific discussions within the R&D organization.
· MSc or PhD in data science, computer science, computational biology, or a related quantitative field.
· 4+ years of hands-on experience in applied data science, machine learning, or algorithm development.
· Strong Python skills and practical experience with common data science and ML libraries such as pandas, NumPy, scikit-learn, PyTorch, SQL, and Spark.
· Proven experience building end-to-end data science solutions, from raw data processing to model development, evaluation, and implementation.
· Experience working with large, messy, real-world datasets and transforming them into reliable analytical or machine learning pipelines.
· Strong understanding of machine learning concepts, model evaluation, statistical analysis, and experimental design.
· Experience with cloud-based computing environments and scalable data processing pipelines.
· Strong analytical and problem-solving skills, with attention to details and scientific rigor.
· Strong research mindset and ability to independently learn new domains, methods, and technologies.
· Excellent communication skills and ability to collaborate effectively across clinical, product, engineering, and data science teams.
· Experience working with biomedical, healthcare (RWD/EHR), pharmaceutical, biomedical ontologies, or real-world patient data.
· Experience developing LLM-based workflows or agentic pipelines.
· Experience with knowledge graphs, graph embeddings, GNNs, or graph-based ML.
· Experience with PySpark, PyTorch, PyGeometric, MLflow, or similar ML engineering tools.
· Experience creating interactive data applications, dashboards, or internal tools.
· Familiarity with clinical trial design, drug development, computational/system biology, or translational medicine.