Quantitative Analyst

ATTIX APAC PTE. LTD.; Singapore; On-site; SGD 150,000 - 230,000; Full time

Job summary

Attix APAC Pte Ltd in Singapore seeks a Quantitative Analyst to advance research across alpha signals and strategy development. You will collaborate with portfolio managers, engineers and product teams to translate ideas into live trading models, with ownership over the research lifecycle from hypothesis to live capital.

The role emphasizes hands-on backtesting, rigorous validation, and performance attribution, in a small fast-moving team focused on delivering measurable investment improvements.

Qualifications

• Advanced degree in a quantitative discipline or equivalent experience.
• 3-7 years in quantitative research or related field.
• Strong Python and SQL programming.
• Solid stats, econometrics, time-series knowledge.
• Backtesting experience with awareness of biases.
• Derivatives knowledge and portfolio theory understanding.

Responsibilities

• Conduct alpha signal research and develop strategies end-to-end.
• Backtest and validate trading ideas with realistic costs and constraints.
• Collaborate with PMs, engineers and product teams to productionise research.
• Monitor model performance and retrain/retire models as needed.

Skills

Python

SQL

Statistics

Time-series

Backtesting

Machine Learning

Communication

Education

Advanced degree in quantitative discipline

Tools

AWS S3

Athena

Version control

Job description

About Attix

Attix APAC Pte Ltd is at the forefront of AI-driven quantitative finance, building sophisticated machine learning and LLM products that power intelligent trading decisions. We research and run systematic strategies across equities and options, and deliver them to clients through our in-house advisory and wealth products. We're looking for a Quantitative Analyst to join our growing team in Singapore and contribute to the research that drives our investment performance.

The Role

As a Quantitative Analyst at Attix, you will conduct research across the full investment lifecycle — from signal generation and strategy development through portfolio construction, implementation and performance attribution. You will partner with portfolio managers, engineers and product teams to take research from hypothesis through to live capital, and you will remain accountable for how that research performs once deployed.

This is a hands-on research role in a small, fast-moving team. You will have significant ownership over the problems you take on and direct visibility into how your work affects returns.

Key Responsibilities

Alpha & Signal Research

• Conduct research into alpha signals across equities and options, covering the full signal lifecycle: idea generation, feature construction, testing, implementation and ongoing monitoring
• Construct and evaluate factors derived from fundamental, market, macro and alternative data sources
• Apply statistical and machine learning techniques to feature discovery, selection and combination
• Assess signal efficacy, decay and capacity, and determine which signals merit inclusion in live models

Strategy Development & Backtesting

• Develop, backtest and implement systematic trading strategies across asset classes
• Design and maintain rigorous backtesting methodology, including cross-validation, walk-forward testing, and realistic assumptions for transaction costs, slippage and capacity
• Evaluate candidate strategies on both standalone performance and their contribution to existing portfolios
• Apply disciplined controls against overfitting, look-ahead bias, survivorship bias and multiple-testing effects

Portfolio Construction & Asset Allocation

• Research and implement portfolio construction methodologies, including mean-variance optimisation, risk parity, hierarchical approaches and robust or resampled methods
• Support strategic and tactical asset allocation across model portfolios
• Quantify portfolio risk, exposures and concentration using factor risk models
• Develop allocation frameworks that respond to changing market conditions, correlation structure and drawdown
• Execute portfolio rebalances and generate trade lists consistent with model views

Model Validation & Performance Monitoring

• Monitor deployed models for performance decay, drift and changing market sensitivity
• Define the criteria and evidence thresholds that determine when a model should be retrained or retired
• Document model methodology, assumptions and limitations to support internal review and governance
• Investigate performance anomalies and distinguish genuine model degradation from market conditions, data quality issues or execution effects

Macro & Market Regime Research

• Research market regime classification, sector rotation and cross-asset relationships
• Translate macro and sentiment research into positioning and allocation views
• Validate the persistence and stability of the relationships that inform allocation decisions

Collaboration & Communication

• Partner with AI/ML engineers, product managers and traders to move research from hypothesis into production
• Communicate research findings clearly to both technical and non-technical audiences
• Document methodology, assumptions and results so that research is reproducible by colleagues
• Contribute to research standards and mentor junior team members

Qualifications

Required

• Advanced degree in a quantitative discipline — mathematics, statistics, physics, engineering, computer science, financial engineering or economics — or equivalent practical experience
• 3-7 years of relevant experience in quantitative research, systematic investing, or a closely related quantitative finance role
• Strong programming ability in Python (pandas, NumPy, scikit-learn, statsmodels or equivalent) and SQL
• Solid foundation in statistics, econometrics, probability and time-series analysis
• Demonstrated experience designing and running backtests, with a rigorous understanding of how they can mislead
• Working knowledge of derivatives, options pricing models, the greeks and volatility behaviour
• Understanding of modern portfolio theory, optimisation methods, risk metrics and performance attribution
• Experience working with large financial datasets, including data quality investigation
• Strong written and verbal communication skills

Preferred

• Experience taking a model or strategy into production and supporting it in a live environment
• Familiarity with machine learning applied to financial prediction, and the challenges of low signal-to-noise data
• Knowledge of market microstructure, execution algorithms and transaction cost analysis
• Experience with factor risk models
• Experience with cloud data infrastructure (AWS S3, Athena or equivalent) and version-controlled research workflows
• Exposure to LLM-based tooling and its application to investment research
• CFA, CQF, FRM or comparable professional qualification

Personal Attributes

• Intellectual rigour and honesty in evaluating your own results
• High initiative and ownership mentality, comfortable taking research from idea through to live capital
• Meticulous attention to detail, particularly around data integrity and validation methodology
• Strong problem-solving skills and the ability to work independently on open-ended questions
• Deep curiosity about financial markets and what drives returns
• Adaptability to work in a fast-paced, evolving environment