Location
London

Hours
Full Time

Salary
Competitive, commensurate with experience

About the Role
Theia Insights builds foundational financial intelligence products, including industry classification, knowledge graphs, and factor risk models for institutional investors. We serve some of the largest asset managers, hedge funds, index providers, and sell-side banks. As an engineer on the Data Products team, you will own the pipelines that ingest NLP and financial data from global public equities to produce the Theia Insights Industry Classification (TIIC) and the datasets behind our Thematic Factor Risk Models (TFM). The Data Products team is a small, senior group that values correctness and reproducibility over volume and works closely with product leads who shape the methodology. We prioritize durability and good judgement over familiarity with the flashiest tools.

In this role, you will build and maintain pipelines that classify global public equities across a five-level taxonomy—sector, industry, sub-industry, major theme, and micro theme—by extracting information from filings and web content and assigning thematic exposures. You will run large-scale NLP and LLM inference (entity extraction, classification, knowledge graph construction) over company documents with cost- and throughput-aware batch execution. You will ingest market data and publish datasets to external distributors, own schema and contract evolution for datasets with real downstream consumers, and collaborate with economists and engineers to turn modelling decisions into reliable production data.

Experience
- Strong production Python skills
- Working with datasets in pandas and Parquet/Arrow, plus analytical engines such as DuckDB or warehouses like Snowflake
- Experience operating orchestrated batch pipelines (Dagster or Airflow), S3-based data flows, and a habit of testing outputs for correctness rather than only for exceptions
- Applied ML in production: embeddings and semantic similarity, clustering, or operationalising models (not necessarily training from scratch)
- AWS fluency and CI/CD discipline

About you
- You have owned pipelines that run on schedules against real volume and have been responsible for their reliability
- You value correctness, reproducibility, and durability over flashy tools
- You are comfortable making cost and throughput trade-offs and setting standards for data quality testing
- Senior candidates typically have experience setting team standards and making architectural decisions

Qualifications
Nice to have:
- Practical LLM engineering: prompting, batch inference, and cost/throughput trade-offs across providers
- Experience with SageMaker, Bedrock, or comparable managed ML tooling
- Financial and equities domain knowledge: classification taxonomies, factor models, index construction (valuable but learnable)
- Infrastructure as code experience (AWS CDK or Terraform) and Docker