Quantitative Developer

Key Responsibilities

• Design and implement robust data pipelines and tools that bring quantitative research into production (e.g., Dagster, Spark, AWS).
• Partner with research teams to ensure style factor research outputs are scalable, testable, and integrated into broader systems.
• Own problem-solving tasks such as automating research workflows, enabling scalable data access, or resolving cross-system compatibility issues.
• Contribute as a generalist across the entire pipeline—from ingestion and transformation to orchestration and tooling.
• Maintain a high standard of engineering quality across data handling, software design, and research tooling.
• Work autonomously while acting as a reliable partner to quantitative researchers, identifying gaps, solving integration issues, and suggesting improvements.

Qualifications

• Strong Python development skills, emphasizing clean, testable, and efficient code.
• Deep understanding of data manipulation libraries such as Pandas and Polars.
• Experience working with SQL and non-SQL databases such as Postgres, Redis, or Mongo.
• Familiarity with distributed computing frameworks such as Apache Spark.
• Hands-on experience with AWS or similar cloud platforms.
• Previous experience in quantitative research environments (financial, academic, or ML-driven).
• Experience supporting production workflows, ideally using modern orchestration tools such as Dagster or Airflow.
• Ability to think holistically across systems and ensure alignment across the research and production stack.
• Strong independent problem-solving instincts.

Why Is This a Great Opportunity

• You are core to the investment engine.
• This is not a support role. The quant and technical team sits inside the investing system and works directly with PMs, quants, risk, and trading.
• Your work impacts PnL, decision quality, and speed.
• You are building the machinery that actually runs money.
• Clean sheet environment with real ownership.
• The firm was built from scratch starting in 2023. Systems, tooling, and workflows are still being designed and improved.
• Integration beats silos.
• This is explicitly an anti-pod model. Fundamental and quantitative professionals operate as one team.
• Engineers and quant developers are expected to understand the investment context, not just tickets.
• You get exposure to how ideas move from research to portfolio to execution, end to end.
• Elite leadership with scale and credibility.
• The founders ran and built at Citadel at the highest level. They know what works and what breaks at scale.
• You get that institutional rigor without the bureaucracy of a mature multi-manager.
• Real assets, real momentum.
• Licensed with $3.5B and scaled to about $11.6B by Q1 2025 with only 6 clients.
• This is not a concept fund or a rebuild story. Capital is stable, growth is real, and the platform is already operating at meaningful scale.
• Engineering that matters.
• This role is heavy on Python and systems that touch alpha capture, transaction cost analysis, research tooling, and production deployment.
• You are the technical glue between models and execution.
• The work is practical, high impact, and directly tied to investment outcomes.
• Broad exposure without being spread thin.
• The firm runs a market neutral, multi strategy equity book across 6 sectors, but with focused coverage and fewer pods.
• You see a wide range of problems without the chaos of dozens of disconnected teams competing internally.
• High bar, serious peers.
• The team spans data science, AI, engineering, investing, risk, and trading.
• The expectation is that everyone makes everyone else better and faster.
• If you want to work around people who care about quality, clarity, and constant improvement, this environment delivers that.
• Strong fit for a Python first quant developer.
• You are a strong Python engineer who understands equities and wants to be closer to the investment process without being a pure researcher or PM.
• This role is well scoped.
• It values technical depth, market understanding, and the ability to ship production grade systems that quants actually use.
• Bottom line. This is a chance to build core investing infrastructure at a scaled but still evolving fund, with elite leadership, real capital, and zero tolerance for wasted effort.