About the Role
:We’re looking for a pragmatic, detail-oriented Software Engineer to help build and maintain our Quantitative Investment Strategy (QIS) Calculation Engine — the core platform that powers the calculation of quantitative indices (levels and decompositions), risk modeling, scenario analysis and prototyping across Global Markets. You’ll work across a modern C# codebase with critical Python interop for research pipelines, numerical routines, quick prototyping, backtesting, and data science tooling.
Expect to collaborate closely with quants, traders, and software engineers to deliver resilient, event driven, auditable systems at scale.
What You’ll Do:
Own the engine:
Design, implement, and optimize components of the QIS engine (e.g., factor models, signal pipelines, backtesting, scenario analysis, portfolio optimization).
Interop & integrations:
Build robust C#/Python interop layers (Pythonnet or equivalent) to bridge production services with research scripts generated by quant team.
Data engineering for quant:
Develop reliable market data ingestion, normalization, and metadata/versioning (symbols, corporate actions).
Performance & reliability:
Profile and tune CPU/memory, parallelism (TPL/async), caching, and I/O; ensure deterministic, reproducible runs with comprehensive logging and telemetry.
Model lifecycle management:
Operationalize quant models—parameterization, configuration, feature flags, release management, and controlled experiment frameworks.
Testing & quality:
Implement rigorous unit/integration tests, regression suites against golden datasets, and CI/CD pipelines.
Risk & compliance:
Embed controls for auditability, explainability, and traceability of results; support model validation and governance that conforms to BMO standards.
Collaboration:
Partner with quants on specification and model translation; work with team on deployment, observability, and production incident response.
Qualifications
Must-have
3+ years professional software engineering experience in C#/.NET building production services/libraries.
Previous experience in Python with an emphasis on Python internals.
Strong knowledge of software architecture and distributed systems: APIs, messaging, concurrency, resiliency patterns, configuration management.
Hands-on experience with Python for numerical computing (Num Py, pandas, Sci Py).
Solid CS fundamentals: data structures, algorithms, complexity analysis, threads/async, networking, serialization.
Database experience: RDMS (Postgre
SQL/MSSQL) fundamentals as well as experience in building queries, database design and optimizing/tuning DB for performance.
Familiarity with market data: time series, corporate actions, calendars, B-PIPE.
Nice-to-have
Experience in finance: quantitative investment models, factor investing, portfolio optimization (e.g., mean-variance, risk parity), transaction cost modeling.
Performance profiling (Benchmark Dot Net , dot Trace, perf counters), and high-performance C# (Span/Memory, SIMD, channels).
Experience with on-prem infrastructure (dedicated VMs, Ansible, etc...), secrets management, and observability (Open Telemetry, Prometheus/Grafana, ELK).
Exposure to modern C# features (.NET 8+), and design patterns for domain-driven design.
Database experience: columnar/time-series stores (Parquet/Delta/Influx
DB/Kdb+/One Tick) a plus.
Proficiency in testing and CI/CD: xUnit/NUnit, test containers, Git Hub Actions/Azure Dev Ops or similar.
What Success Looks Like (6–12 Months)
Enhance the QIS engine’s index calculation throughput and increase the speed at which new indices can be added within the engine
Deliver a clean interop layer with clear contracts and automated validation between Python and production C#.
Develop and ship at least one new quantitative investment strategy with demonstrable notional attribution.
Our Tech Stack
Core: C#/.NET 8 with ASP.NET Core, on-prem VMs, IIS
Interop:
Python (Num Py/pandas/Sci Py) with Pythonnet
Tooling:
Azure Dev Ops, Open Telemetry
Testing: xUnit/NUnit
Why Join Us
Impact at scale:
Your work powers real strategies, capital deployment, and risk decisions across Global Markets.
R&D velocity:
Tight feedback loops with quants and…
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