ATTIX APAC PTE. LTD. · Singapore ·
Quantitative Analyst conducting full-lifecycle research, signal generation, and strategy development for systematic trading using Python, SQL, and machine learning techniques.
CFRA · Bangalore, Karnataka
wellington · Boston, MA, United States
wellington · Boston, MA, United States
Fidelity Investments · Boston, MA
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.
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.
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
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
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
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
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
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
Proven and verifiable live trading track record of at least 1 year for either a portfolio or a strategy.
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
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
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
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