Position: Clinical Data Scientist

About QuantHealth: 

QuantHealth (QH) is a growing AI startup in the clinical trial space, leveraging AI and big data to simulate and optimize trials for pharmaceutical companies. Our ability to combine large biomedical datasets with vast amounts of patient data allows our customers to simulate their clinical trials in a SaaS environment to reduce cost, shorten development time and improve the probability of clinical trial success.    

About the role:  

As a Clinical Data Scientist, you will play a pivotal role in developing and delivering impactful AI-driven solutions for our customers. You will collaborate closely with clinical, engineering, and algorithm teams to address clinical challenges and translate them into actionable insights powered by our AI platform. This dynamic role focuses on advancing our clinical simulation and predictive capabilities.  

The ideal candidate brings several years of applied data science experience in clinical or biomedical domains, including building predictive algorithms and data pipelines. The candidate should be comfortable working with diverse data sources such as electronic health records (EHR), knowledge graphs (KG), and medical literature mining. The position involves analyzing large datasets using the Python data stack, developing data science solutions, and contributing to the enhancement of our clinical trial simulation platform. The role requires a strong research orientation and scientific thinking to drive methodological innovation and evidence-based solutions. 

 Responsibilities: 

• Design, develop, and validate predictive models and simulation tools to support clinical and biomedical research use cases. 

• Collaborate with clinical, product, and engineering teams to prototype and implement data-driven solutions within our AI platform. 

• Explore and integrate diverse clinical data sources (e.g., EHR, medical literature, biomedical ontologies, KG) to build scalable and robust data pipelines to automate our clinical simulation workflows. 

• Conduct exploratory data analysis, algorithm benchmarking, and hypothesis testing to drive new insights and platform improvements. 

• Implement LLM and ETL automated processes to enhance QH data and simulations.    

• Utilize statistical techniques to evaluate models and optimize clinical study design. 

• Develop and continuously improve our data science workflows using modules like PySpark, PyTorch, PyGeometric and MLflow. 

• Stay current with emerging methodologies and technologies in clinical data science and contribute to internal knowledge-sharing initiatives. 

Qualifications: 

• Advanced degree (MSc or PhD) in bioinformatics, computational biology, data science, computer science, or a related field. 

• 3+ years of experience in clinical or biomedical data science, with a strong track record of working with real-world healthcare or other biomedical data. 

• Hands-on experience developing machine learning models using clinical datasets such as EHR, knowledge graphs, pathology reports, and/or biomedical literature. 

• Proficient in Python with extensive experience using machine learning and data processing tools (e.g., pandas, scikit-learn, SQL, Spark). 

• Experience with cloud-based computing and scalable data pipelines. 

• Strong analytical and problem-solving skills, with attention to detail and scientific rigor. 

• Strong research mindset aimed at solving complex, novel problems. 

• Excellent communication skills and a collaborative mindset. 

Advantage: 

• Experience with LLM pipeline development.  

• Experience with Knowledge graphs and embedding algorithms.   

• Experience with creating interactive data applications.