23507 - Quantitative Research Analyst

We are looking for a Quantitative Research Analyst

This person is the human gate between a backtest result and a claim AP is willing to publish to a paying financial-services client.

They decide which discovered signals are promoted, which are deprecated, whether an accuracy number is honest, and how the methodology is explained to sophisticated buyers.

Remote, Israel, must overlap US EST market hours (pre-market through post-close)

Responsibilities:

Own the system framework, responsible for the end-to-end signals system that turns raw signals into validated, production-ready predictions, and for the rigor of every stage in between.

• Make promote/hold/deprecate decisions on all candidate signals
• Interrogate promotion evidence and statistical rigor
• Validate holdout and placebo testing before production release
• Distinguish genuine inverse signals from artefacts

Backtesting and research design

• Own the walk-forward backtesting framework and challenge its design
• Design and test compound research hypotheses
• Own and evolve the signal promotion threshold policy
• Guard against overfitting, leakage, survivorship, and multiple-comparisons abuse

Prediction quality and calibration

• Assess live forecast accuracy and calibration
• Own the accuracy-vs-coverage trade-off
• Set and tune the abstention policy
• Benchmark performance against naive baselines

Monitoring and decay

• Monitor signal decay and govern deprecations
• Maintain integrity of the outcome-resolution pipeline

Client-facing methodology

• Write and defend client-facing methodology documentation
• Communicate statistical concepts to commercial/editorial stakeholders
• Serve as technical authority in pre-sales and onboarding

Requirements:

• 5+ years in a quantitative research, quantitative analyst, systematic strategy or financial data science role, with demonstrable ownership of signal research — not solely model implementation.
• Statistical rigour: Working command of hypothesis testing, multiple-comparisons correction (Benjamini–Hochberg or equivalent), rank correlation, ROC/AUC, calibration and proper scoring rules.
• Must be able to explain what a q-value guarantees that a p-value does not.
• Practical experience with walk-forward and purged cross-validation, look-ahead bias prevention, holdout design and regime-dependent performance.
• Financial markets literacy: Comfortable with equity index and ETF return data, forward-return construction, trading horizons, volatility regimes and macro context (VIX, yield curve, FRED series).
• Python: Fluent in Python 3.11+ with pandas, NumPy, scipy, statsmodels and scikit-learn — sufficient to reproduce, modify and extend the discovery and evaluation code, not merely to consume its output.
• SQL: Able to write non-trivial analytical SQL directly against PostgreSQL to interrogate signals, predictions and resolution coverage without waiting on an engineer.
• Intellectual honesty: A track record of killing their own results. This role exists to prevent AP publishing a false edge; scepticism must be a reflex, and must survive commercial pressure.
• Communication: Able to produce written methodology that stands up to a buy-side reader, and to explain it verbally to an executive audience.
• Experience with NLP-derived or alternative-data signals (news, sentiment, filings, satellite, transactional) 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, abstention/selective-prediction frameworks, or cost-sensitive decision thresholds.
• Prior work in a commercial data-product context where methodology was client-visible and contractually relevant.
• Working knowledge of GCP (BigQuery, Vertex AI, Cloud Run) or an equivalent cloud analytics environment.
• Graduate degree in statistics, financial engineering, econometrics, physics, mathematics or a comparable quantitative discipline.