Forecasting Scientist, Population Behavior & Calibration

Aaru; New York (NY); On-site; USD 120,000 - 190,000; Full time

Benefits offered by this job

Equity participation

Medical, vision & dental coverage

Visa sponsorship and relocation

Competitive base salary

Job summary

Aaru is building simulations of human behavior in New York, and is seeking a Prediction Researcher to own difficult questions about aggregate outcomes. You will formulate hypotheses, curate datasets, build predictive methods, and design evaluations that survive holdouts and changing conditions.

Your work may combine structured data, statistical learning, probabilistic modeling, language models, retrieval, tools, and explicit agent simulation.

Qualifications

• You have a record of rigorous work in machine learning, forecasting, statistics, econometrics, quantitative research, decision science, or a related field.
• You have built predictive systems from messy, heterogeneous data and evaluated them against observed outcomes.
• You are comfortable moving between statistical reasoning, model design, data construction, implementation, experiment design, and detailed error analysis.
• You communicate clearly, including when the evidence is negative, fragile, or inconsistent with your hypothesis.

Responsibilities

• Own a high-value research question in forecasting, aggregate behavioral prediction, calibration, conditioning, subgroup decomposition, drift, or agentic prediction.
• Turn ambiguous questions into falsifiable hypotheses, strong baselines, appropriate datasets, decisive experiments, and explicit criteria for success.
• Build prediction methods from real-world records such as transactions, product usage, event histories, operational data, market data, surveys, customer data, and longitudinal outcomes.
• Combine language models with structured data, retrieval, tools, quantitative models, and inference-time reasoning when the combination produces measurable value.
• Develop estimates of population behavior and determine how those estimates vary with attributes, prior behavior, information exposure, environment, time, and intervention.
• Design temporal holdouts and prospective tests that use only information available at the time a prediction would actually have been made.
• Measure calibration, proper scoring, ranking quality, selective prediction, subgroup performance, robustness, and the decision cost of different errors.
• Compare complex methods with strong alternatives, including historical rates, conventional statistical models, direct aggregate prediction, segment-level methods, and explicit population simulation.

Skills

Machine learning

Forecasting

Statistics

Econometrics

Quantitative research

Job description

Aaru is building simulations of human behavior in New York, and is seeking a Prediction Researcher to own difficult questions about aggregate outcomes. You will formulate hypotheses, curate datasets, build predictive methods, and design evaluations that survive holdouts and changing conditions.

Your work may combine structured data, statistical learning, probabilistic modeling, language models, retrieval, tools, and explicit agent simulation.