{"id":1242620,"url":"https://alion.io/job/attix-apac-quantitative-analyst","title":"Quantitative Analyst","company":{"id":3802234,"name":"Attix Apac","domain":"attix.com","url":"https://alion.io/company/attix-apac","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":"middle","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":7500,"max":12000,"currency":"SGD","period":"month","gross":true,"usd_annual":112752},"salary_estimate":null,"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Volatility","optional":false}],"status":"live","first_seen_at":"2026-09-24T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-09-24T00:00:00Z","board_verified":false,"closed_at":null,"days_open":22,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":22},"description":"About Attix\nAttix 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.\nThe Role\nAs 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.\nThis 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.\nKey Responsibilities\nAlpha & Signal ResearchConduct research into alpha signals across equities and options, covering the full signal lifecycle: idea generation, feature construction, testing, implementation and ongoing monitoring\n\nConstruct and evaluate factors derived from fundamental, market, macro and alternative data sources\n\nApply statistical and machine learning techniques to feature discovery, selection and combination\n\nAssess signal efficacy, decay and capacity, and determine which signals merit inclusion in live models\n\nStrategy Development & BacktestingDevelop, backtest and implement systematic trading strategies across asset classes\n\nDesign and maintain rigorous backtesting methodology, including cross-validation, walk-forward testing, and realistic assumptions for transaction costs, slippage and capacity\n\nEvaluate candidate strategies on both standalone performance and their contribution to existing portfolios\n\nApply disciplined controls against overfitting, look-ahead bias, survivorship bias and multiple-testing effects\n\nPortfolio Construction & Asset AllocationResearch and implement portfolio construction methodologies, including mean-variance optimisation, risk parity, hierarchical approaches and robust or resampled methods\n\nSupport strategic and tactical asset allocation across model portfolios\n\nQuantify portfolio risk, exposures and concentration using factor risk models\n\nDevelop allocation frameworks that respond to changing market conditions, correlation structure and drawdown\n\nExecute portfolio rebalances and generate trade lists consistent with model views\n\nModel Validation & Performance MonitoringMonitor deployed models for performance decay, drift and changing market sensitivity\n\nDefine the criteria and evidence thresholds that determine when a model should be retrained or retired\n\nDocument model methodology, assumptions and limitations to support internal review and governance\n\nInvestigate performance anomalies and distinguish genuine model degradation from market conditions, data quality issues or execution effects\n\nMacro & Market Regime ResearchResearch market regime classification, sector rotation and cross-asset relationships\n\nTranslate macro and sentiment research into positioning and allocation views\n\nValidate the persistence and stability of the relationships that inform allocation decisions\n\nCollaboration & CommunicationPartner with AI/ML engineers, product managers and traders to move research from hypothesis into production\n\nCommunicate research findings clearly to both technical and non-technical audiences\n\nDocument methodology, assumptions and results so that research is reproducible by colleagues\n\nContribute to research standards and mentor junior team members\n\nQualifications\nRequiredAdvanced degree in a quantitative discipline — mathematics, statistics, physics, engineering, computer science, financial engineering or economics — or equivalent practical experience\n\n3-7 years of relevant experience in quantitative research, systematic investing, or a closely related quantitative finance role\n\nStrong programming ability in Python (pandas, NumPy, scikit-learn, statsmodels or equivalent) and SQL\n\nSolid foundation in statistics, econometrics, probability and time-series analysis\n\nDemonstrated experience designing and running backtests, with a rigorous understanding of how they can mislead\n\nWorking knowledge of derivatives, options pricing models, the greeks and volatility behaviour\n\nUnderstanding of modern portfolio theory, optimisation methods, risk metrics and performance attribution\n\nExperience working with large financial datasets, including data quality investigation\n\nStrong written and verbal communication skills\n\nPreferredExperience taking a model or strategy into production and supporting it in a live environment\n\nFamiliarity with machine learning applied to financial prediction, and the challenges of low signal-to-noise data\n\nKnowledge of market microstructure, execution algorithms and transaction cost analysis\n\nExperience with factor risk models \n\nExperience with cloud data infrastructure (AWS S3, Athena or equivalent) and version-controlled research workflows\n\nExposure to LLM-based tooling and its application to investment research\n\nCFA, CQF, FRM or comparable professional qualification\n\nPersonal AttributesIntellectual rigour and honesty in evaluating your own results\n\nHigh initiative and ownership mentality, comfortable taking research from idea through to live capital\n\nMeticulous attention to detail, particularly around data integrity and validation methodology\n\nStrong problem-solving skills and the ability to work independently on open-ended questions\n\nDeep curiosity about financial markets and what drives returns\n\nAdaptability to work in a fast-paced, evolving environment\n\nSkills\nMarket Research, Financial Research, Time Series Analysis, Requirement Specification, Strategy Development, NumPy, Transactional Analysis, Investment Analysis, scientific discipline, Economics, Computer Science, Implement Trading Strategy, SQL, Portfolio Management, Python, Econometrics, integrate Data sources, Financial Data, Volatility, Feature 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