Quant Strategist

Team: Macro Quant Research   |   Location: Singapore   |   Type: Full-time

Reporting to: Chief Quant Strategist and co-COO

The mission

We are building an in-house platform that turns unstructured market information — sell-side research, central-bank communications, news/social flow, prediction markets, time-series analysis, and market pricing — into timely, structured signals for a macro trading team. The platform already includes LLM-assisted research ingestion, live market monitoring, statistical dislocation reports, lead-lag analytics, and a central-bank policy engine.

The platform has been built and run by the Chief Quant Strategist (who is also co-COO). We are hiring a Quant Strategist to help run and extend it, owning specific workstreams end to end under the Chief Quant Strategist's direction. This is a hands-on individual-contributor role with a clear path to broader platform ownership for the right person. It is a builder's role, not a ticket-taking one.

What success looks like in the first six months

Success means becoming a dependable second pair of hands, ultimately becoming a builder the desk relies on. Concretely:

·        You independently own two or three workstreams end to end, and they run reliably.

·        Your outputs — reports, briefings, alerts — are accurate, on time, and trusted by the desk.

·        You have measurably improved at least one analytic: better statistics, better signal-to-noise, or better reliability.

·        You are visibly growing toward broader ownership, taking on more of the platform over time.

What you'll actually do

·        Own and extend quant research reports. Take specific statistical-dislocation, lead-lag, and correlation reports across rates, FX, equities, and futures, and make them better and more reliable under the Chief Quant Strategist's direction.

·        Run and improve live monitors and briefings. Help operate the scheduled news/social sweeps, prediction-market trackers, and macro briefings delivered into Teams and email, with a focus on actionable signal-to-noise rather than volume.

·        Contribute to the central-bank and research-parsing stacks. Extend the multi-bank policy-monitoring and meeting-scorecard engine and the sell-side research parser as directed.

·        Build and validate data pipelines. Wire in approved data sources and build the scheduled jobs behind the reports — and apply the sanity, reconciliation, and robustness checks that keep the outputs trustworthy.

·        Ship to portfolio managers and traders. Produce clean PDFs, tables, Teams posts, and email alerts that are accurate, on time, and easy to act on.

·        Learn the platform's standards. Absorb how validation, statistical rigour, and clean engineering are done here, and increasingly design them yourself.

Core must-haves

·        Strong Python. You write clean, reliable Python for data work — pipelines, scripts, analysis — with good habits around structure, error handling, and version control.

·        Time-series analysis and statistical soundness. Statistical rigour is a first principle of this role, not a finishing step. You are fluent with pandas/numpy-style manipulation and measures such as z-scores, correlations, stationarity, and principal component analysis — and, more importantly, you understand the ways time-series results can be false, overstated, or fragile, especially on the thin, low-frequency samples typical of macro data, and you design your analysis to guard against them.

·        Financial-markets grounding and curiosity. You can hold a useful conversation about rates, FX, equities, and futures, and you are hungry to go deeper. You understand — or are visibly driven to understand — what a macro trader cares about.

·        Comfortable building with LLMs. You have used LLMs to build or analyse (extraction, summarization, tooling) and grasp, at a working level, their strengths and failure modes — hallucination, drift, and the need to verify.

·        Data and tooling fundamentals. You are comfortable on the command line and fluent with SQL and Excel, and you can work with databases and structured data. (Standing up your own infrastructure — Docker, Postgres — is a plus, not a requirement.)

·        Rigour and ownership. You take a task from vague to done, you care about correctness because the output may inform trader decisions, and you dislike hacks and silent failures. You are coachable, and you want to grow.

Useful pluses

·        Production engineering: services, scheduled jobs, Docker/Postgres, reproducible environments, no technical debt.

·        Building real LLM applications with evaluation, guardrails, and tracing; local/open-weight models, RAG, embeddings.

·        Designing validation and evaluation frameworks, and statistical robustness testing, rather than only applying them.

·        Full-stack or front-end development, including LLM-accelerated build that you can then verify.

·        Bloomberg or buy-side market-data systems; central-bank / rates-market knowledge (OIS, meeting-date forwards, policy pricing).

·        Advanced derivatives pricing concepts; prediction-market, alternative-data, or social/news monitoring experience.

·        Teams, webhook, email, SharePoint/OneDrive, or report-generation automation.

Confidentiality and judgment

This role touches confidential research, internal views, credentials, and trading-adjacent workflows. We need someone who protects sensitive data by default, understands when material must stay on-premises or local, and treats LLM output as something to verify, not blindly trust.

Who thrives here

You like small teams, direct accountability, and practical impact. You are rigorous by instinct: you would rather report no signal than a spurious one, and you are suspicious of results that look too good on thin macro data. You care that a briefing is right and on time, and you take feedback well and turn it into better work fast. You are excited to build on a platform that already runs live, to learn its standards for statistics and clean engineering, and to grow into owning more of it over time.

How to apply

Send a CV and a short note on the most substantial data or quant pipeline you have built end to end: what it ingested, what it delivered, who used it, and what you would improve. Please avoid including confidential code, data, or client information.