Data Scientist AI for Biodiversity Knowledge Platform

Natural History Museum; Greater London; Hybrid; GBP 65,000 - 90,000; Full time

Benefits offered by this job

27.5 days holiday + bank holidays

Pension scheme (DC)

Season ticket/transport benefits

Life insurance

Exhibition access & discounts

Staff discount

Training & development opportunities

CSSC membership

Sports & Social Association

Job summary

Natural History Museum in London seeks an experienced Data Scientist for a 12‑month project to build an AI-powered knowledge extraction platform. You will work with OCR-derived text, annotation workflows, data quality assurance, and prepare datasets for NER and LLM extraction.

You will contribute to reproducible data pipelines using Python, collaborate with scientists, curators, and international partners, and help translate biodiversity literature into a searchable research resource.

Qualifications

• Strong Python skills and experience building reproducible data and ML pipelines.
• Experience with OCR-derived/unstructured text, annotation workflows, entity normalisation and data quality assurance.
• Preparing datasets for NER, LLM-based extraction, training and evaluation.
• Familiarity with structured outputs, schema validation and knowledge graphs is desirable.

Responsibilities

• Develop an AI-powered knowledge extraction platform for biodiversity literature.
• Support OCR processing, annotation workflows, data quality assurance and dataset preparation for LLM extraction.

Skills

Python

Data pipelines

OCR processing

NER/LLM preparation

Knowledge graphs

Job description

Natural History Museum in London seeks an experienced Data Scientist for a 12‑month project to build an AI-powered knowledge extraction platform. You will work with OCR-derived text, annotation workflows, data quality assurance, and prepare datasets for NER and LLM extraction.

You will contribute to reproducible data pipelines using Python, collaborate with scientists, curators, and international partners, and help translate biodiversity literature into a searchable research resource.