Information Security Analyst jobs in United Kingdom

Our mission is to restore cell health and resilience through cell rejuvenation to reverse disease, injury, and the disabilities that can occur throughout life.

We believe that diverse perspectives are foundational to scientific innovation and inquiry. At Altos, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining a diverse and inclusive environment.

Use AI agents to make complex research data FAIR—Findable, Accessible, Interoperable, Reusable—so scientists and product teams can ask richer questions, move faster, and advance discovery. Be part of a team using knowledge and data engineering to enable the transition from manual to LLM‑enabled, agentic data ingestion and curation. You’ll sit at the intersection of data curation, data and knowledge engineering. Your job is to automate the ingestion and standardization of multi‑source datasets into governed, searchable, analytics-ready assets, and to model the domain knowledge that ties them together.

Responsibilities

• Curate and harmonize data. Ingest, profile, clean, normalize, and annotate multi‑modal research datasets (e.g., genomics/transcriptomics, proteomics, imaging/microscopy, CRISPR screens, assay/instrument metadata). Map to controlled vocabularies and standards; manage identifiers, synonyms, and crosswalks.
• Deliver insights from curated data. Focus on the substance—entities, relationships, and annotations that answer real research and product questions using public domain assets from Ensembl, GEO, PubMed, OMIM, OLS, amongst others. Use pipelines and existing data sources storage pragmatically as tools to deliver content and outcomes.
• Model knowledge to serve decisions. Capture the concepts and links researchers actually use; keep schemas lightweight and purpose‑built. Leverage OBO Foundry ontologies; define with LinkML; align to the BioLink/Biolink Model; and integrate/serve with platforms such as BioCypher.
• Quality, governance & AI enablement. Instrument automated checks (tests/expectations), process development to improvement data FAIRification, and LLM‑assisted validations; capture provenance/lineage; codify SOPs; and work to facilitate the migration of processes from manual → automation → agentic (MCP‑integrated) workflows.
• Serve as a key technical liaison between scientific, data science, and engineering teams, translating complex research needs into scalable and maintainable data solutions.
• Define and evangelize best practices for data and knowledge engineering across the organization, mentoring junior team members and building reusable, AI-enhanced, enterprise‑level components.

• PhD, Biological Sciences, Computer Science, Software Engineering, or related quantitative field, or equivalent technical experience
• Candidates should have relevant experience in data curation, ontology/knowledge engineering, or data engineering (or equivalent experience) at a biotechnology company.
• Mindset: You prioritize data and business objectives over tools; technology is a means to an end.
• Demonstrably strong Python expertise, particularly in the context of data modeling and processing, with strong skills in both relational (SQL) and graph data stores, and the ability to choose pragmatically between them (e.g., Postgres/Redshift vs. Neo4j/Neptune).
• Comfortable building pragmatic ETL/ELT workflows in a major cloud (preferably AWS), using orchestration frameworks or AWS-native tools.
• Active user of AI coding editors such as Cursor, with an active interest in designing and building Model Context Protocol (MCP) applications; motivated to migrate processes from manual → automation → agentic.
• Mature understanding of data quality, provenance, versioning, and “curation as code,” including hands-on use of testing/validation frameworks.

• Experience in basic/exploratory life‑science research across multiple modalities (genomics/transcriptomics, proteomics, imaging/microscopy, screening, model organisms); a user of curated content to achieve research/business outcomes.
• Experience with a data platform such as lamin.ai.
• Experience with vector databases and search (e.g., Weaviate, FAISS, pgvector) and AI/LLM frameworks (e.g., LiteLLM, LangChain, LlamaIndex) for retrieval-augmented generation and agent workflows.
• Experience with OBO Foundry ontologies and modern frameworks such as LinkML, BioLink, and BioCypher, familiarity with graph database technologies (e.g., Neo4j, AWS Neptune) and semantic standards (OWL, RDF, SPARQL).
• Experience creating lightweight semantic layers and AI/LLM‑assisted curation workflows (LiteLLM, FastMCP).

The salary range for Cambridge, UK:

Exact compensation may vary based on skills, experience, and location.

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We value collaboration and scientific excellence. We believe that diverse perspectives and a culture of belonging are foundational to scientific innovation and inquiry. At Altos Labs, exceptional scientists and industry leaders from around the world work together to advance a shared mission. Our intentional focus is on Belonging, so that all employees know that they are valued for their unique perspectives. We are all accountable for sustaining an inclusive environment.

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