Head of Quant & Data Science

How You’ll Make an Impact

Quantitative Strategy and Model Development

• Set the quantitative agenda for Fitch Solutions: define which models, scores and analytics create commercial differentiation, and sequence their delivery.
• Design, build and validate models across the fixed income estate, including credit risk and relative value analytics, spread and pricing models, private credit and business development company analytics, loan and bond level metrics, and portfolio level aggregation.
• Own model methodology documentation, validation standards and performance monitoring, with an audit trail suitable for review by clients and internal risk functions.
• Work with Head of Analytics to establish standards for reproducibility, back testing and version control so that model outputs published to clients can be explained and defended.

Data and Feature Infrastructure

• Define and build the data infrastructure that supports quantitative work at scale: feature stores, time series stores, reference data alignment and the pipelines that keep them current.
• Partner with the Head of Data Products and the Chief Data Office to ensure the unified taxonomy, metadata schema and entity model are fit for quantitative consumption.
• Set the reference architecture for how analytic outputs are published back into products, ensuring consistency of structure, identifier and vintage regardless of originating business unit.
• Hold the organisation accountable for the data quality that models depend on, with measurable inputs to the enterprise data quality framework.

Knowledge Graph Infrastructure

• Own the design and delivery of the Fitch Solutions knowledge graph: the entity, instrument, issuer, fund and document relationships that connect content across business units.
• Define the graph schema, ontology extensions and entity resolution logic that allow issuers, obligors, facilities and instruments to be linked reliably across internal and third party sources.
• Build the retrieval layer that allows semantic search, retrieval augmented generation and agentic workflows to operate over the graph with accurate grounding and attribution.
• Work with the Head of AI Products to ensure graph and vector infrastructure develop as a single coherent capability rather than parallel efforts.

Product and Commercial Delivery

• Partner with Product Heads across the four business units to convert client problems into quantitative product features with defined success metrics.
• Engage directly with enterprise clients, including asset managers, banks, insurers, private credit managers and corporates, on methodology, model performance and data integration.
• Support commercial teams in technical evaluations, proofs of concept and renewal conversations where quantitative credibility is decisive.
• Assess build against buy for third party analytics and vendor models, and make clear recommendations to the Chief Product Officer.

Team Leadership

• Build and lead a team of three to five data scientists and quantitative developers, with scope to grow as the portfolio expands.
• Set hiring standards, technical review practice and career development paths for quantitative staff in an organisation where the function is being established rather than inherited.
• Establish working practices that allow a small team to serve four business units without becoming a bottleneck: shared libraries, documented interfaces and clear prioritisation.
• Represent quantitative work to senior leadership and the wider Fitch Group technology community, translating technical trade offs into commercial language.

You May be a Good Fit if

• Substantial experience leading data science or quantitative research teams in financial markets, financial data, asset management, banking or a comparable analytics business.
• Deep domain knowledge of fixed income products. Meaningful depth in private credit, business development companies, leveraged loans and corporate bonds is strongly preferred.
• Demonstrable record of building production models consumed by external clients, not only internal research or one off analysis.
• Hands on technical strength in Python and SQL, with working command of the modern data stack and of graph query languages such as SPARQL, Cypher or equivalent & strong knowledge of data surface infrastructures such as Snowflake/Databricks etc.
• Practical experience designing knowledge graph, ontology or linked data infrastructure and the entity resolution logic that underpins it.
• Understanding of fixed income identifier standards and their interoperability, including ISIN, CUSIP, SEDOL, LEI and vendor specific schemes, and experience working with major data providers such as Bloomberg, LSEG, FactSet or ICE Data Services.
• Experience operating across organisational boundaries and influencing without direct authority across product, technology, data and research functions.
• Ability to translate quantitative work into commercial outcomes that are legible to non technical stakeholders, including at executive and client level.
• Comfort in a dynamic environment where priorities move, structures are still forming and progress depends on initiative rather than process.

What Would Make You Stand Out

• Experience with retrieval augmented generation and agentic architectures, and clear judgement on the data requirements they impose.
• Exposure to private credit data challenges specifically: sparse disclosure, inconsistent reporting, valuation opacity and manager level idiosyncrasy.
• Track record commercialising analytics, including pricing, packaging and client onboarding for quantitative products.
• Familiarity with credit