Senior Data Scientist (Platform Development)

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 lead the development of QuantHealth’s internal platform for streamlining, automating, and standardizing key customer delivery workflows.

This is a hybrid role at the intersection of data science, software engineering, ML systems, and internal product development. The ideal candidate is a strong builder who can take ownership of complex workflows, understand complex real-world data and modeling processes, and turn them into reliable, reusable, scalable systems.

The internal platform will serve as a central layer for improving how customer delivery work is planned, executed, automated, and reused across projects. It will bring together key workflows, automatic agentic systems, data assets, computational processes, reusable components, and quality-control mechanisms into a more integrated and scalable operating model. The goal is to reduce manual effort, improve consistency, shorten delivery timelines, increase transparency, and create reusable institutional knowledge across projects. This role requires someone who is comfortable moving between data science, architecture, software development, data pipelines, AI-driven automation, and internal applications. It also requires collaboration with delivery, clinical, engineering, and data science teams. It is a senior individual contributor role with high ownership and broad impact.

Responsibilities

• Lead the design and development of a set of internal tools that streamline, automate, and scale the company's delivery workflows and model development lifecycle.
• Build 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.
• Design solutions that support versioning, traceability, access control, and reuse of data and analytical assets.
• 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, machine learning, data engineering, or a hybrid data science / systems-building 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 solutions that people actually use.
• 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 leading the development of internal platforms, workflow automation systems, data products, or operational tools for technical teams.
• Experience building LLM-based and agentic applications, retrieval systems, or automated reasoning workflows.
• Experience with access control, identity management, audit logs, data lineage, or regulated-data environments.
• 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