Quantitative Researcher — AI Reasoning, Consensus Scoring & Forecast Evaluation

iPulse AI is an Open Agentic Investment Research Platform being developed by Future Edge Group.

We believe better investment decisions can help direct capital toward the companies, technologies, and ideas capable of building a stronger future. When investors can clearly understand a company’s products, objectives, financial position, strategy, risks, and broader mission, markets can allocate resources more intelligently—and promising businesses can earn support on the strength of transparent evidence.

Our research principle is clear:

Without bias. Grounded in evidence. Comparable by design.

We build quantitative research methods, AI-assisted analysis, consensus scoring, portfolio intelligence, and transparent publication systems that help investors examine opportunities from multiple independent perspectives.

Our goal is to remove noise, make disagreement visible, expose uncertainty and limitations, and distinguish evidence from promotion—not promise certainty or tell the world what to buy.

This is not a traditional quantitative-finance position centered on mathematics alone.

Our investment-research workflows combine quantitative models with AI-generated analysis, behavioral-finance considerations, independent research perspectives, and multi-step reasoning. Evaluating the quality of that reasoning—its evidence, assumptions, logical consistency, behavioral biases, omissions, and final conclusions—is central to this position.

You will improve the mathematical and evaluative foundations of the iPulse AI Consensus Score. Your work will help make forecasts and asset rankings better calibrated, more explainable, and more robust across asset classes, forecast horizons, and changing market regimes.

This is a full-time, onsite position in New York City for someone who can operate across quantitative finance, artificial intelligence, behavioral finance, statistical evaluation, and rigorous investment research.

• Research and improve ensemble weighting, forecast calibration, disagreement measures, volatility adjustments, confidence scores, and rating thresholds.
• Develop frameworks for evaluating AI-generated investment research, including intermediate claims, cited evidence, assumptions, counterarguments, and final conclusions.
• Assess research rationales for logical consistency, factual grounding, behavioral bias, unsupported inference, missing evidence, and material omissions.
• Design common evaluation metrics that allow forecasts and research outputs from different AI advisors, models, and methodologies to be compared consistently.
• Develop reproducible backtests and out-of-sample evaluations for forecast accuracy, reasoning quality, calibration, ranking stability, regime sensitivity, drift, and failure modes.
• Investigate how behavioral-finance effects—including overconfidence, anchoring, herding, loss aversion, narrative bias, and recency bias—may affect models, research agents, and investor interpretation.
• Examine rare but important long-horizon reasoning failures that may not be visible through conventional accuracy metrics alone.
• Design adversarial tests, counterfactual evaluations, ablation studies, and stress scenarios for AI-assisted investment-research workflows.
• Help determine when advisor disagreement represents valuable independent information and when it reflects noise, weak grounding, or methodological failure.
• Translate validated research into production specifications, evaluation rubrics, monitoring metrics, methodology documentation, and transparent score explanations.
• Work with AI engineers, data specialists, portfolio professionals, and product teams to move research from experimentation into reliable investor-facing systems.
• Document limitations and negative findings as rigorously as successful improvements.

• Strong foundations in statistics, probability, time-series analysis, numerical methods, quantitative finance, and experimental design.
• Advanced proficiency in Python and SQL. Although we will no longer require you to write any code yourself. We are fully onboard with ChatGPT and Claude.
• Practical and theoretical familiarity with behavioral finance.
• A strong understanding of modern AI systems in both theory and practice, including LLM evaluation, reasoning models, agentic workflows, probabilistic forecasting, or ensemble methods.
• Experience with forecasting, portfolio analytics, factor models, probabilistic calibration, systematic investment research, ranking systems, or model evaluation.
• The ability to evaluate not only whether a forecast was correct, but whether the research process was grounded, logically coherent, appropriately uncertain, and reproducible.
• Scientific discipline around leakage controls, baselines, out-of-sample testing, statistical significance, reproducibility, and model-risk documentation.
• Intellectual honesty and a willingness to publish limitations, uncertainty, and failed approaches alongside successful improvements.
• Clear technical writing and the ability to communicate complex quantitative and AI concepts to investment and product audiences.

We look for a sustained record of excellence, not simply impressive titles.

Strong signals may include:

• National or international mathematics competitions
• Chess championships or distinguished tournament performance
• Quantitative-research distinctions
• Peer-reviewed research
• Exceptional academic performance
• Meaningful open-source contributions
• Rigorous research implementations
• Successful quantitative or AI products
• Institutional investment or portfolio experience
• Measurable leadership in technically demanding environments

For candidates with a software or AI engineering background, a substantial body of inspectable work is highly valued. This may include meaningful open-source contributions, widely used libraries, production-grade AI systems, influential technical writing, or equivalent professional systems.

Commit counts and fashionable tools are not enough. We evaluate technical depth, originality, reliability, intellectual rigor, and real-world impact.

Prestige may begin the conversation. Demonstrated excellence—and commitment to the mission—earns a place on the team.

• Bachelor’s, master’s, or doctoral degree in quantitative finance, mathematics, statistics, computer science, economics, physics, operations research, machine learning, or a closely related discipline—or equivalent exceptional professional evidence.
• Demonstrated experience conducting rigorous quantitative or AI research.
• Ability to work onsite in New York City