Predictive Modeling Contractor

Lancesoft; South San Francisco (CA); Remote; USD 145.000 - 303.000; Vollzeit

Zusammenfassung

Lancesoft seeks a Predictive Modeling Contractor to support the DAISAE platform and RDE 9 teams. The role focuses on AI rapid prototyping, data foundations, and predictive modeling across drug safety timelines, including early molecule evaluation through post-marketing phases.

Requires MSc/PhD in quantitative fields, strong ML/AI prototyping skills, and experience with multi-modal biomedical datasets. Remote options may be available for suitable candidates.

Qualifikationen

• Background in AI rapid prototyping and predictive modeling.
• Experience handling multimodal biomedical datasets (omics, clinical, RWD).
• Familiarity with agentic AI systems and MCP tool development.
• Agile prototyping mindset with software engineering for data science.

Aufgaben

• Prototype AI tools and MCP tool integrations for DAISAE.
• Build and refine predictive models using multimodal datasets to support safety risk prediction.
• Translate prototype findings into architectural requirements for shared data foundations.
• Collaborate with data scientists, safety scientists, and partners for alignment with priorities.
• Support governance, validation, and production rollout of components.

Kenntnisse

Predictive modeling

Machine learning

AI prototyping

Agentic AI

LLM tools

MCP tools

Biomedical data

Data science engineering

Ausbildung

MSc in Data Science

PhD in Computational Biology

Tools

Ontology tooling

Knowledge Graphs

Jobbeschreibung

Role Title: Predictive Modeling Contractor (RDE 9 / DAISAE)

Target Workstream: RDE 9 / RDE 9B2 (DAISAE Platform & Predictive Modeling Subteams)

Preferred Location: South San Francisco (SSF), CA;open to Basel, Switzerland or remote depending on candidate fit

Duration: November 1, 2026 December 31, 2027 (approx. 14 months)

Approved Budget: CHF120, 000 for remainder of 2026 / CHF250, 000 for 2027

Position Summary

We are seeking a contractor with a background combining AI rapid prototyping and Predictive Modeling to support the DAISAE platform and predictive analytics subteams within the RDE 9 program. This role focuses on co-developing shared data and technical foundations (e.G. Ontologies, Knowledge Graphs), prototyping agentic AI capabilities, and advancing predictive models for drug safety risk prediction across the product lifecycle.

Key Responsibilities

• AI Prototyping & MCP Tool Development: Rapidly explore, evaluate, and prototype emerging AI tools and agentic capabilities, specifically developing MCP tools and AI agent integrations for DAISAE.
• Predictive Modeling: Build and refine predictive models using multi-modal datasets (including in vitro, in vivo, omics, clinical trial data, real-world data (RWD), and spontaneous safety reports) to enable risk prediction from early molecule-level evaluation to late-stage and post-marketing patient safety.
• Architecture & Platform Design: Translate feasibility findings and prototype evaluations into concrete architectural design and platform requirements for shared data and technical foundations.
• Co-Development & Governance Collaboration: Work in an agile way to prototype reusable technical capabilities before transitioning components to RDT (*** Digital Tech) for formal validation, compliance, and production rollout.
• Cross-Functional Alignment: Collaborate with data scientists, safety scientists, and RDT technical partners to ensure technical execution aligns with business priorities.

Qualifications & Experience

Education:

MSc or PhD in Data Science, Computer Science, Computational Biology, Bioinformatics, Biostatistics, Epidemiology, or a related quantitative field.

Technical Expertise:

Strong expertise in predictive modeling, machine learning, and AI prototyping. Hands-on experience developing agentic AI systems, LLM tools, and MCP tools. Proficiency in handling biomedical datasets (omics, clinical trials, RWD, or in vitro/in vivo data). Agile prototyping mindset with software engineering skills tailored for data science.

Domain Knowledge:

Background in pharmacovigilance, drug safety, toxicology, or quantitative risk management is highly desirable.

Soft Skills:

"AI native" approach to problem-solving with adaptability to fast-moving frameworks. Proactive attitude and strong communication skills suitable for global matrix team settings.