Data Analyst Research I

Planet Pharma; South San Francisco (CA); On-site; USD 83,000 - 98,000; Full time

Job summary

Planet Pharma is seeking a contractor to advance AI rapid prototyping and predictive modeling across the DAISAE platform. You will prototype shared data foundations, develop MCP tools, and refine predictive models for drug safety risk from early molecule evaluation to post-marketing stages.

The role requires MSc/PhD in a quantitative field and hands-on experience with biomedical datasets, agentic AI systems, and agile software practices.

Qualifications

• Education: MSc or PhD in Data Science, Computer Science, Computational Biology, Bioinformatics, Biostatistics, Epidemiology, or related quantitative field.
• 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.
• Background in pharmacovigilance, drug safety, toxicology, or quantitative risk management is highly desirable.
• AI native problem-solving with adaptability to fast-moving frameworks; proactive attitude and strong communication skills.

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 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: Prototype reusable technical capabilities before transitioning components to DT for validation, compliance, and production rollout.
• Cross-Functional Alignment: Collaborate with data scientists, safety scientists, and DT technical partners to ensure technical execution aligns with business priorities.

Skills

Predictive modeling

AI prototyping

Biomedical datasets

Agile prototyping

Education

Master's/PhD in data science or related

Job description

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 DT (Digital Tech) for formal validation, compliance, and production rollout.
• Cross-Functional Alignment: Collaborate with data scientists, safety scientists, and DT 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.

Pay Rate Range: $60-71/hr depending on experience