Quantitative Research Analyst

Quantitative Research Analyst

This is a quantitative research role in the buy-side sense of the word. You will be responsible for the alpha content of a live market-signals product: deciding what constitutes a real, tradable signal, proving it with statistics that would survive a due-diligence meeting, and standing behind the numbers when a sophisticated financial client asks how they were produced.

What you will do:

• Own the signal set
• Make the promote, hold or deprecate decision on every candidate signal produced by the discovery process.
• A full run evaluates thousands of candidates across taxonomy groupings, markets and horizons — your judgement is the gate between a backtest and a published claim.
• Interrogate promotion evidence rather than accepting it: rank information coefficient, AUC, directional hit rate, precision at K, temporal stability, and false-discovery-rate-adjusted significance against minimum observation counts.
• Confirm every promoted signal survives a locked out-of-sample holdout and a placebo battery (shuffled dates, shuffled labels, future-shifted timestamps) before it reaches clients.
• Separate genuine inverse relationships — negative IC is common and legitimate in risk and geopolitical themes — from artefacts, and confirm sign handling is correct at prediction time.
• Backtesting and research design
• Own the walk-forward backtesting framework in practice: expanding folds, purge and embargo gaps to prevent look-ahead, per-fold aggregation, and combination of evidence across folds.
• Challenge the design where it is too permissive or too conservative.
• Design and test compound research hypotheses — multi-factor combinations, sentiment-conditioned filters, geographic constraints, and volatility-regime conditioning.
• Own the promotion threshold policy.
• Recommend evidence-backed changes and quantify the false-discovery cost of loosening any criterion.
• Guard against the classic failure modes: multiple comparisons, survivorship, data leakage from enrichment, and regime-specific overfitting.
• Prediction quality and calibration
• Measure live forecast quality honestly — headline accuracy, accuracy by market and by horizon, Brier score, and reliability curves with expected calibration error.
• Own the accuracy-versus-coverage trade-off.
• A high accuracy figure only means something on a defined high-confidence slice; determine and defend the confidence threshold at which the target holds, with the coverage cost stated explicitly.
• Set and tune the abstention policy — when the model should decline to call a market — balancing selectivity against commercial usefulness.
• Benchmark against naive baselines (always-neutral, always-long) and refuse to report an edge that does not beat them.
• Monitoring and decay
• Track promoted signals for decay using rolling IC, Z-scores, changepoint detection and slope-change diagnostics; confirm or override automatic deprecations.
• Maintain the health of the resolution pipeline that converts forecasts into realized outcomes, since every accuracy metric depends on it.

Required:

• Quantitative research experience. 5+ years in a quantitative research, quantitative analyst, systematic strategy or financial data science seat — at a hedge fund, asset manager, proprietary trading firm, bank quant desk, or a financial data or alternative-data provider.
• You must have owned signal or factor research, not solely implemented someone else's model.
• Financial markets fluency. Genuine comfort with equity index and ETF return series, forward-return construction, trading horizons, volatility regimes, and macro context.
• Statistical rigour. Working command of hypothesis testing, multiple-comparisons correction (Benjamini–Hochberg or equivalent), rank correlation, ROC/AUC, calibration and proper scoring rules.
• You should be able to explain what a q-value guarantees that a p-value does not.
• Time-series discipline. Hands-on experience with walk-forward and purged cross-validation, look-ahead bias prevention, holdout design, and regime-dependent performance.
• Python as a research tool. Fluent with pandas, NumPy, SciPy, statsmodels and scikit-learn — enough to reproduce, modify and extend research code independently.
• You are not expected to build production services.
• SQL. Able to write non-trivial analytical SQL to interrogate signals, forecasts and coverage without waiting on an engineer.
• Intellectual honesty. A demonstrable track record of killing your own results.
• This role exists to prevent the publication of a false edge; scepticism has to be a reflex, and it has to survive commercial pressure.
• Communication. Able to write methodology that stands up to a buy-side reader and explain it verbally to a non-quantitative executive audience.

Preferred:

• Experience with news, sentiment, filings or other alternative-data signals and their particular failure modes.
• Familiarity with gradient-boosted ensembles (LightGBM, CatBoost, XGBoost), stacking with out-of-fold predictions, and isotonic or Platt calibration.
• Exposure to conformal prediction, selective-prediction or abstention frameworks, or cost-sensitive decision thresholds.
• Prior work on a commercial data product where the methodology was client-visible and contractually relevant.
• Working knowledge of a cloud analytics environment (GCP BigQuery / Vertex AI or equivalent).
• Graduate degree in statistics, financial engineering, econometrics, mathematics, physics or a comparable quantitative discipline.
• CFA, CQF or FRM is a plus but not a substitute for research experience.

Benefits: Why QualityAI?

QualityAI is an AI-first quality engineering company helping enterprises deploy and scale complex systems with greater confidence. Operating across data, models, platforms, infrastructure, and operational environments, the company provides assurance and engineering expertise that helps organizations ensure systems perform reliably in real-world conditions. Formerly Qualitest, QualityAI supports global enterprises across regulated and technology-driven industries, combining deep engineering heritage with AI-enabled delivery, operational assurance, and lifecycle expertise to help clients achieve certainty at go-live.

Be a part of a company who strives to support for diversity and inclusion in the workplace - we are one, we are many at QualityAI. Celebrate culture, share knowledge with engineers from around the globe, and inspire each other through our differences. Local and global opportunities - we offer you internal rotation and international mobility opportunities to grow your career. Clear view of your career and progression with the company - QualityAI is growing massively (since Jan 2021 - added more than 2000 engineers) and giving you the opportunity to grow with us. Never stop experimenting and learning with QualityAI Tech academy: 3000+ training courses, mentorship programs, technical tribes, sponsored certifications, leadership programs and much more. Earn bonuses via our Client Referral and Employee Referral Program's. Refer and earn - tap your network for net-worth.