Lead Applied AI Scientist

New

The Analytics Research Institute, LLCApplied AI

Full-time, remote within the United StatesFull-TimeLead

SalarySalary range: $140,000–$170,000, depending on qualifications and experience.

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Job Details

Hold an advanced degree in computer science, machine learning, data science, biomedical informatics, statistics, or a related field, or have equivalent experience.
Have substantial experience applying ML/AI methods to complex, real-world problems.
Demonstrate strong Python skills.
Have a track record of building well-tested, documented, reproducible analytical software.
Have a solid grounding in ML fundamentals, including experimental design, model evaluation, uncertainty, and error analysis.
Have experience setting evaluation criteria and benchmarking against meaningful baselines.
Have practical experience with LLMs, NLP, embeddings, semantic retrieval, or RAG.
Have experience with reproducibility, explainability, provenance, auditability, or other responsible-AI requirements.
Be able to identify model limitations and communicate them to technical and non-technical audiences.
Be able to independently verify code, methods, and outputs produced with generative AI tools.
Be able to lead a technically complex project from problem definition through validation and delivery.
Have collaboration skills and an interest in mentoring other staff.

Design and build AI methods for analyzing administrative data related to scientific research portfolios.
Translate scientific, analytical, and client requirements into testable technical approaches.
Build prototypes and production systems using LLMs, NLP, embeddings, semantic retrieval, RAG/GraphRAG, knowledge graphs, and agentic workflows.
Benchmark AI methods against simpler analytical approaches, established ML methods, and trained human reviewers.
Move validated methods from exploratory testing into robust, maintainable production with engineering partners.
Help define evaluation standards for reproducibility, explainability, robustness, bias, and hallucination risk.
Design workflows that trace results to source data, methods, and prompts and can be reproduced by another analyst.
Contribute to validation reports and documentation for internal review and client assessment.
Provide technical input, review code, and mentor data scientists through methodological guidance.
Run focused experiments on applied AI and retrieval/knowledge-graph methods, and contribute to proposals, demos, white papers, and publications.

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