NLP Scientist Claim Accuracy and Compliance

 
The Fountain Group is currently seeking to fill 2 NLP Scientist openings for Claim Accuracy and Compliance with a prominent client of ours.

Ability to work onsite in South San Francisco as needed is preferred. Open to remote candidates able to overlap work hours with PST.
 

Job Title: NLP Scientist — Claim Accuracy and Compliance
Pay Rate: Market Rate - Open Bid. For exceptionally talent individuals hourly w2 $100-$200/hr
Duration: 6 months to start

Job Description

• Develop evidence-grounded NLP systems that verify generated claims against approved evidence sources.

• Build retrieval pipelines to identify relevant evidence from approved claims, product labeling, clinical studies, publications, and technical references.

• Decompose compound statements into independently verifiable assertions and evaluate evidence support for each claim.

• Implement entailment and claim-verification workflows that distinguish supported, qualified, contradicted, and insufficiently supported claims.

• Design abstention, confidence-threshold, and escalation mechanisms for uncertain evidence.

• Build traceable decision systems that preserve model versions, evidence sets, citations, and reviewer actions.

• Develop expert-labeled evaluation datasets with annotation guidelines and inter-annotator agreement measurement.

• Evaluate false-approval and false-rejection rates separately for high-stakes claim verification.

• Integrate human review workflows for Medical, Legal, and Regulatory use cases.

Education

• Bachelor’s or Master’s degree in Computer Science, Computational Linguistics, Natural Language Processing, Artificial Intelligence, or a related technical field.

Required Skills

• Advanced Python production engineering with modern NLP frameworks.

• Hands-on experience with evidence-grounded NLP, including hybrid retrieval, natural-language inference, entailment, claim decomposition, and evidence attribution.

• Experience evaluating whether generated answers are genuinely supported by cited sources.

• Experience working with scientific, technical, regulatory, legal, or similarly structured source material.

• Expertise in vector and lexical information retrieval.

• Experience with model APIs and production NLP evaluation infrastructure.

• Experience creating expert-labeled datasets, annotation guidelines, and inter-annotator agreement metrics.

• Experience implementing confidence thresholds, abstention, escalation rules, and safe-failure mechanisms.

• Experience building reproducible and auditable ML decision systems.

Preferred Skills

• Experience combining deterministic rules with ML/model-based judgment.

• Knowledge graph experience linking claims, evidence, references, products, or indications.

• Experience in regulated or high-stakes environments such as legal, financial compliance, scientific publishing, or fact-checking.

• Familiarity with clinical/scientific study design, statistical evidence, and citation methodology.

• Pharmaceutical or life-sciences NLP experience.

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