About QuantHealth
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 helps customers simulate clinical trials, reduce development risk and cost, shorten timelines, and improve the probability of clinical trial success.
About the Role
We are looking for a Senior Data Scientist to join the Clinical Data Science R&D team. The ideal candidate is a strong builder and technical leader who can take ownership of complex workflows, understand real-world data and modeling processes, and turn them into reliable, reusable, and scalable systems.
You will play a leading 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 can take ownership of complex, ambiguous problems end to end, from data exploration and feature engineering through model development and validation to building scalable pipelines, agentic systems, and AI-driven workflows.
You will lead cross-functional collaboration with clinical experts, product, delivery, engineering, and other data science teams to translate challenging clinical and biomedical questions into robust, data-driven solutions. A major focus of the role will be designing and building agentic workflows and automation systems that improve and scale clinical research and development processes.
The ideal candidate brings several years of hands-on experience in applied data science, machine learning, and production-grade data pipeline development. We also value strong project ownership, technical leadership potential, scientific thinking, and the ability to independently learn and operate in complex domains. Experience with biomedical or clinical data, as well as experience building LLM-based applications, orchestration frameworks, or agentic AI workflows, is a strong advantage.
Responsibilities
- Lead the development of data-science-driven systems that combine data pipelines, AI-driven automation, analytical workflows, and internal applications.
- Develop LLM-based and agentic workflows to automate complex operational and analytical processes.
- Implement validation, monitoring, and quality control mechanisms to improve reliability, reproducibility, and trust in delivery outputs.
- Collaborate closely with delivery, clinical, data science, product, and engineering teams to translate operational challenges into scalable technical solutions.
- Own key technical design decisions and contribute to the long-term direction of the internal delivery platform.
- Improve the reliability and maintainability of existing internal tools, workflows, and automation processes.
- Create clear documentation, standards, and best practices for using and extending the system.
- Act as a senior technical contributor, providing guidance and mentorship to junior team members on data science workflows, automation design, code quality, and systems-building best practices.
Qualifications
- MSc or PhD in computer science, data science, computational biology, or a related quantitative/technical field.
- 6+ years of hands-on experience in applied data science and machine learning role.
- Strong experience developing complex data science solutions, including data pipelines, automation workflows, model evaluation, and production-oriented implementation.
- Strong Python skills, with the ability to write clean, modular, reliable code for data science and automation workflows.
- Experience working with large, complex, real-world datasets and turning them into reliable data products, pipelines, or analytical workflows.
- Experience integrating data, models, APIs, or internal tools into practical end-to-end solutions.
- Experience with cloud environments, containerization, CI/CD, and production deployment workflows.
- Experience building user-facing automation tools or internal applications that collect user input, guide workflows, and translate user needs into reliable automated processes.
- Strong understanding of machine learning concepts, model evaluation, analytical validation, and experimental design.
- Ability to work with ambiguous requirements, break down complex operational and analytical workflows, and translate them into practical technical solutions.
- Strong product sense for internal tools: ability to understand users, simplify workflows, and build valuable solutions.
- Strong analytical and problem-solving skills, with attention to detail, reliability, and reproducibility.
- Excellent communication skills and ability to collaborate across data science, engineering, clinical, product, and delivery teams.
Strong Advantages
- Experience building LLM-based and agentic applications, retrieval systems, or automated reasoning workflows.
- Experience working with healthcare, clinical, biomedical, pharma, or real-world patient data.
- Experience working in a startup environment where ownership, speed, and pragmatic engineering are critical.
