What are the responsibilities and job description for the Scientific Data Architect position at ClinLab Solutions Group?
Work Location: Middlesex County, Massachusetts
Summary:
Seeking a driven and product-focused technical expert to design and implement AI-native scientific data solutions. This role involves collaborating with scientists, product managers, and engineers to transform complex scientific data into actionable outcomes, with a focus on biopharma R&D and data integration for advanced analytics and AI/ML applications.
Responsibilities:
- Engage directly with scientific end users to understand data challenges and requirements, building strong relationships and accelerating tailored solutions.
- Design and implement scalable, reusable data models to efficiently organize scientific data for diverse use cases.
- Translate scientific workflows into robust solutions using advanced data platforms and tools.
- Prototype and implement solutions including data model design, parser development, lab software integration, and data visualization/app development in Python.
- Collaborate with analysts, scientists, and AI engineers to develop and deploy a range of models (ML, AI, mechanistic, statistical, hybrid).
- Iterate dynamically with stakeholders through regular demos and meetings to drive solution adoption and continuous improvement.
- Communicate progress proactively and deliver solution demonstrations to stakeholders.
- Work with product teams to prioritize development roadmaps and rapidly learn new technologies to support evolving scientific use cases.
Qualifications:
- PhD with 7 years or Master’s with 10 years of industry experience in life sciences, with deep domain knowledge in drug discovery, preclinical development, CMC, or product quality testing.
- Demonstrated experience defining, designing, prototyping, and implementing AI/ML-driven use cases in cloud environments.
- Strong background collaborating with cross-functional teams, including product managers, engineers, and scientific stakeholders.
- Expertise in exploratory data analysis and workflow optimization for scientific outcomes.
- Excellent communication and storytelling skills, with experience engaging both scientific and executive audiences.
- Consulting experience advising scientists to advance research, development, and quality testing.
- Hands-on experience with Python, data model design (tabular & JSON), parser development, API integrations, and data visualization frameworks (e.g., Streamlit, holoviews, Plotly).
- Ability to rapidly learn new tools, technologies, and scientific domains as needed.
- Strong sense of ownership, self-discipline, and determination in building extensible data models and applications for scientific users.