AI/ML Engineer / Data Scientist (Predictive Modeling Contractor)

The Fountain Group; South San Francisco (CA); Hybrid; USD 83,000 - 98,000; Full time

Job summary

The Fountain Group is seeking an AI/ML Engineer / Data Scientist contractor to rapidly prototype AI capabilities and advance predictive models for drug safety. This role supports cross-functional teams, develops shared data foundations, and collaborates with RDT and safety scientists.

Remote work is open, with possible hybrid or occasional onsite in South San Francisco. Pay rate is $60–$71.07 per hour on W2 for a 14-month contract with a strong possibility of extension, emphasizing agile

Qualifications

• Masters degree or higher in a quantitative field is required.
• Expertise in predictive modeling, ML, and AI prototyping.
• Hands-on experience building agentic AI systems and MCP/LLM tools.
• Experience handling biomedical datasets (omics, clinical, RWD).

Responsibilities

• Prototype AI tools and agentic capabilities for DAISAE.
• Develop predictive models across multi-modal datasets to support drug safety risk prediction.
• Translate feasibility findings into architectural design and platform requirements.
• Collaborate with cross-functional teams in an agile environment.

Skills

AI prototyping

Predictive modeling

Agentic AI

MCP tools

Biomedical datasets

Agile prototyping

Education

MSc or PhD in quantitative field

Tools

LLM tools

MCP tools

Job description

The Fountain Group is currently seeking an AI/ML Engineer / Data Scientist (Predictive Modeling Contractor) for a prominent client of ours. Open for 100% Remote Candidates. Prefer someone who can work Hybrid or go onsite occasionally at South San Francisco, CA office. Details for the position are as follows:

Pay Rate - $60 – 71.07/hr on W2

14 months Contract Role (HIGH Possibility for further extension)

Position Summary

We are seeking a contractor with a background combining AI rapid prototyping and Predictive Modeling to support the platform and predictive analytics subteams within the 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 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. Minimum of a Masters Degree required.
• 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.