Analyst, Investments

Location: Singapore

Company Overview

We operate at the intersection of discretionary macro insight and systematic execution, with a strong engineering culture underpinning everything we do.

Role Overview

We are seeking a Quantitative Analyst to join our macro team in Singapore. This is a hybrid research–engineering–trading seat: you will design and deploy systematic strategies that express macro views primarily ininterest rates and FX, while also building and owning the data infrastructureand AI tooling that the wider desk relies on.

The ideal candidate is equally comfortable bootstrapping a swap curve, debugging a real-time ingestion pipeline, and prototyping an LLM-driven research workflow. You should be passionate about monetary policyand cross-asset macro, fluent in time-series econometrics, and a genuinely strong engineer who treats data quality and reproducibility as first-classconcerns.

1. Quantitative Research & Strategy Development(Rates & FX focus)

• Design, backtest, and deploy systematic strategies across rates (OIS/IRS, curve and butterfly trades, carry &roll-down, swaption-implied signals, cross-currency basis) and FX (G10 and AsiaEM spot/forward/NDF, carry, value, momentum, forward-points and vol-surfacesignals).
• Build econometric and statistical modelsincluding mean-reversion, regime-switching, and carry/value/momentum factorframeworks, with explicit attention to monetary-policy reaction functions andcentral-bank event risk.
• Construct and maintain pricing and analyticsprimitives: yield-curve construction and OIS discounting, term-structuremodeling, covered-interest-parity/forward-point decomposition, and FX/ratesvolatility-surface handling.
• Evaluate risk–reward through rigoroussimulation, scenario, and stress testing, with strict controls againstlook-ahead bias, survivorship bias, and point-in-time data leakage.

1. Data Engineering & Infrastructure

• Develop and maintain scalable, fault-tolerantpipelines for ingesting and cleaning real-time and historical macro, rates, FX,and high-frequency datasets (tick/OHLC, curves, vol surfaces, economic releases, central-bank calendars).
• Design and own time-series data architecture —including star-schema / dimensional warehouse design — with point-in-timecorrectness, partitioning, and query performance as core objectives.
• Integrate market-data and execution APIs (e.g.,BidFX, Citi Velocity, Bloomberg) for live signal monitoring and execution,handling vendor licensing constraints, entitlements, and data-governancerequirements.
• Build ingestion for unstructured andsemi-structured sources (vendor research, PDF/email feeds, web sources) and normalize them into the analytics layer.
• Partner with technology and operations tostrengthen backtesting, portfolio analytics, and execution efficiency.

1. AI & Machine Learning

• Apply machine learning to signal generation andregime detection (e.g., gradient boosting, regularized regression,classification for regime states), with disciplined validation and an emphasison out-of-sample robustness over in-sample fit.
• Build and maintain AI-powered research tooling:retrieval-augmented generation (RAG) pipelines over sell-side and internalresearch, embeddings and vector search, and LLM-based synthesis of macro,central-bank, and cross-asset narratives.
• Develop agentic and tool-using LLM workflows(e.g., MCP servers / function-calling) that connect language models to thefirm's data layer under strict read-only and audit controls.
• Apply NLP to macro and policy text (central-bankcommunications, research, news) to extract structured, tradable features.
• Maintain a critical, evidence-driven stance onmodel risk — understanding where ML adds genuine edge versus where simpler,more interpretable approaches are superior.

1. Database & Systems

• Strong, hands-on ownership of relational databases — SQL Server and/or PostgreSQL — including schema design, indexing,query optimization, and stored-procedure/ETL logic against large time-seriestables.
• Experience with cloud-hosted database infrastructure (e.g., AWS RDS) and with NoSQL / document stores (e.g., MongoDB)where appropriate.
• Familiarity with vector databases (e.g., Qdrant,pgvector) for embedding storage and semantic retrieval.
• Comfort working across the data stack: caching(Redis), real-time messaging/streaming, and API services that expose analytics to research and trading consumers.

1. Trading & Risk Management

• Monitor live positions, model exposures, and market behavior to manage intraday and overnight risk.
• Implement stop-loss, take-profit, andvolatility-adjusted sizing frameworks for capital efficiency.
• Conduct post-trade and attribution analysis tocontinuously refine models and trading logic.

1. Cross-Team Collaboration

• Work closely with macro PMs to align systematic models with discretionary rates and FX views.
• Contribute original, quantitatively supportedideas in research meetings.
• Document methodologies and maintain transparency and reproducibility across the strategy lifecycle.

Qualifications

1. Master's degree in Financial Mathematics, Computer Science, Engineering, Physics, Economics, or a related quantitative field.
2. Strong coding proficiency in Python (NumPy, pandas, scikit-learn, statsmodels, multiprocessing); familiarity with a compiled or systems language (e.g., C#/.NET, C++, Rust) is a plus.
3. Solid grasp of time-series analysis, statistical arbitrage, and portfolio optimization.
4. Demonstrable strength in rates and FX fundamentals: curve construction, swaps/OIS, forwards/NDFs, carry, and an understanding of how monetary policy transmits into market pricing.
5. Hands-on experience applying machine learning and AI-driven methods to macro research: regime detection, factor extraction, NLP on central bank communications and research, signal generation from alternative datasets.
6. Familiarity with US macro dynamics - Fed policy, Treasury curve structure, USD funding markets - and how shifts in the US cycle propagate into global rates, FX, and risk sentiment.

1. Hands-on experience designing and operatingproduction databases (SQL Server, PostgreSQL, or equivalent), including schema design and query optimization on large datasets.
2. Experience building and maintaining datapipelines and integrating market-data/execution APIs.
3. Exposure to distributed computing and cloud datainfrastructure.

1. Practical experience applying ML to noisyfinancial or time-series data, with sound validation discipline.
2. Familiarity with modern AI tooling: LLMs, RAG,embeddings/vector search, and agentic/tool-using workflows (MCP, functioncalling).
3. Ability to judge where AI/ML genuinely improvesa process versus where it adds complexity without edge.

1. Excellent analytical, problem-solving, andcommunication skills.
2. Deep interest in global macro, monetary policy,and cross-asset market dynamics.
3. Self-directed, with the discipline to ship andmaintain robust, well-documented systems.

Why RV Capital

Join a high-conviction Asia macro fund that merges fundamental insight with quantitative and engineering precision.

Direct exposure to live trading, portfolio construction, andmacro strategy implementation in rates and FX.

A flat structure that encourages independent research, rapidmodel deployment, and genuine ownership of the data and AI infrastructure thatpowers the desk.

Application Process

To apply, please submit your resume and a cover letter to Seema at savasthi@rvcapital.com with your name written clearly in the subjectline of your application.

RV Capital is an equal opportunity employer. Weencourage candidates from diverse backgrounds to