Quant SME – Contract

5 days per week on site in London

£950-1000 per day via Umbrella

Experience:

• Exposure to Thomson Reuters, Bloomberg applications, Excel Add-ins, etc.
• Hands-on experience in computing multi-asset portfolio statistics
• Building/maintaining a Python library (not app work): packaging, versioning, releasing a package that downstream services depend on. This is the core of the job.
• Time-series data engineering with pandas at scale (multi-index frames, frequency alignment, currency/tenor handling). pandas is the entire data plane here.
• Snowflake / data-warehouse experience: writing SQL against curated vendor tables, EAV vs wide-table schemas. This is how all market data actually arrives — not via Bloomberg/Reuters terminals directly.
• Vendor data onboarding (FactSet, EODHD, Macrobond, Albourne-style feeds) — mapping vendor schemas into a canonical model.

Skills:

• Strong Python 3.11+: dataclasses, type hints, pydantic v2 — not just "knowledge of Python." Set a clear bar (mid/senior).
• Parser / AST / interpreter design — this is a DSL. Comfort with tokenizing, expression trees, immutable AST nodes, and a parse→validate→execute pipeline is the single most repo-specific skill.
• pandas + numpy/scipy proficiency (statsmodels, scikit-learn for the analytics extra).
• GitLab CI/CD specifically (pipelines, not just "use gitlab"): the repo runs lint→test→build→publish stages.
• Testing discipline: pytest, property-based testing (Hypothesis), fixture/regression suites. Given ~230 test files and hermetic catalog-regression, this is essential — I'd make it a first-class skill, not implied.
• Linting/quality tooling: ruff, type checking (pyright/basedpyright).
• YAML-driven configuration/mapping design.
• Optimization libraries: scipy (SLSQP), optionally cvxpy/numba — for the weight-optimization functions.
• API/contract discipline: designing stable interfaces since a separate service consumes this library.
• Knowledge of how to use gitlab
• Knowledge of Software architecture principles
• Team player, supporting front-end, middle-tier teams with API implementations
• AI-assisted / agent-driven development — the repo is heavily tooled with Cursor skills and agent workflows (mapping helpers, changelog automation, gap analysis). A candidate comfortable working alongside these tools will ramp much faster.