Quantitative Research Analyst

Quality Ai; Northern (KY); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Diversity and inclusion programs

Internal rotation and internationalMob

Tech academy and training

Job summary

QualityAI is seeking a Quantitative Research Analyst to own signal sets for a live market-signals product on the buy-side. You will decide what constitutes tradable signals and validate them with statistics, defending methodology to sophisticated clients.

You will design walk-forward backtests, quantify evidence across folds, and guard against data leakage while maintaining rigorous research standards.

Qualifications

• 5+ years in quantitative research or a related role at a hedge fund, asset manager, prop trading firm, bank quant desk, or data provider.
• Strong fluency with equity indices, ETF return series, forward returns, and macro context.
• Rigorous understanding of hypothesis testing, multiple comparisons, ROC/AUC, and calibration.
• Hands-on experience with walk-forward and purged cross-validation, look-ahead bias prevention.
• Proficient in Python and SQL for research; comfortable reproducing and extending code.

Responsibilities

• Own the signal set and decide on promotion/deprecation of signals from discovery.
• Interrogate promotion evidence using IC, AUC, directional hit rate, and FDR-adjusted significance.
• Ensure promoted signals survive out-of-sample holdout and placebo tests before clients.
• Backtest design: walk-forward, embargo gaps, and cross-validation across folds.

Skills

Signal research

Statistics

Walk-forward testing

Time-series analysis

Python

SQL

Data visualization

Communication

Critical thinking

Hypothesis testing

Education

Advanced degree in a quantitative field

Tools

Pandas

NumPy

SciPy

scikit-learn

Job description

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Quantitative Research Analyst

Date: 21 Aug 2026 Company: QualityAI Country/Region: US

About the Role

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 adue-diligencemeeting, 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,holdor deprecate decision on every candidate signal produced by the discovery process. A full run evaluates thousands of candidates across taxonomy groupings,marketsand horizons — your judgement is the gate between abacktestand 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 — fromartefacts, andconfirm sign handling is correct at prediction time.

Backtracking and research design

• Own the walk-forwardbacktestingframework in practice: expanding folds,purgeand 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

• Measureliveforecast 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;determineand defend the confidence threshold at which the target holds, with the coverage coststatedexplicitly.
• 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 ina quantitativeresearch, quantitative analyst, systematicstrategyor financial data science seat — at a hedge fund, asset manager, proprietary trading firm, bank quant desk, ora financialdata oralternative-dataprovider. You must have owned signal or factor research, not solely implemented someone else's model.
• Financial markets fluency. Genuine comfort with equity index and ETFreturnseries, forward-return construction, trading horizons, volatility regimes, and macro context. You should be able to look at a signal and form a view on whether the economic story behind it is plausible.
• Statisticalrigour. Working command of hypothesis testing, multiple-comparisons correction (Benjamini–Hochberg or equivalent), rank correlation, ROC/AUC,calibrationand 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.
• Pythonasa research tool. Fluent with pandas, NumPy, SciPy,statsmodelsand scikit-learn — enough to reproduce,modifyand extend research code independently. You are not expected to build production services.
• SQL. Able to write non-trivial analytical SQL to interrogate signals,forecastsand coverage without waiting on an engineer.
• Intellectual honesty. A demonstrabletrack recordof killing your own results. This role exists to prevent the publication of a false edge;scepticismhas to be a reflex, and ithas tosurvive commercial pressure.
• Communication. Able to writemethodologythat stands up to a buy-side reader andexplainit verbally to a non-quantitative executive audience.

Preferred

• Experience with news, sentiment, filings or otheralternative-datasignals and theirparticular failuremodes.
• Familiarity with gradient-boosted ensembles (LightGBM,CatBoost,XGBoost), stacking with out-of-fold predictions, and isotonic or Platt calibration.
• Exposure toconformal prediction, selective-prediction or abstention frameworks, or cost-sensitive decision thresholds.
• Priorworkon a commercial data product where the methodizes client-visible and contractually relevant.
• Working knowledge of a cloud analytics environment (GCPBigQuery/ Vertex AI or equivalent).
• Graduate degree in statistics, financial engineering, econometrics, mathematics,physicsor a comparable quantitative discipline. CFA,CQFor FRMisa 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.