{"id":661860,"url":"https://alion.io/job/aaru-prediction-researcher","title":"Researcher, Prediction","company":{"id":668926,"name":"Aaru","domain":"aaru.com","url":"https://alion.io/company/aaru","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":80,"open_postings":8,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":10,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["New York, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":200000,"max":600000,"currency":"USD","period":"year","gross":null,"usd_annual":600000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Post-training","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Time Series","optional":false}],"status":"live","first_seen_at":"2026-08-22T15:49:53Z","employer_posted_date":"2026-08-22","last_verified_at":"2026-11-01T17:20:30Z","board_verified":true,"closed_at":null,"days_open":71,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":71},"description":"About Aaru\nAaru builds simulations of human behavior. Each simulation contains a population of AI agents, each representing a person who could plausibly exist in the real world and capable of making decisions within a modeled environment. Companies and institutions use these simulations to test consequential choices before committing-from product launches and pricing decisions to strategic communications and policy changes.\nBuilding a useful simulation requires more than generating plausible text. Populations must represent real people and groups; predictions must be calibrated; simulations must remain coherent as conditions change; and the product must make the resulting evidence legible enough to support real decisions.\nWe are a small, in-person team in New York. We work with urgency, high ownership, and intellectual honesty. We expect people to surface inconvenient evidence, change their minds quickly, and carry important work all the way to a result.\nAbout Prediction Research\nPrediction Research builds systems that estimate future or otherwise unknown outcomes from data. The team's primary object is the population-level outcome: given a population, a question, and the relevant context, what aggregate result should we expect, how uncertain should we be, and how should that estimate change when the conditions change?\nSome problems are best solved with structured statistical or machine-learning methods. Others may benefit from language models, retrieval, tools, explicit decomposition, simulated agents, or a combination of these approaches. The team's job is not to assume that the most complex method is best. It is to determine which information and method produce genuine predictive signal beyond strong, simpler baselines.\nPrediction Research is not prompt engineering and it is not a speculative forecasting exercise. It is empirical predictive science. A prediction of 60 percent should resolve near 60 percent under the conditions where it is made. Improvements must survive temporal holdouts, new populations, changing environments, and prospective outcomes.\nThe role\nAs a Prediction Researcher, you will own difficult, open questions about aggregate human behavior and future outcomes. You will formulate hypotheses, construct or curate datasets, build predictive methods, design evaluations, inspect failures, and communicate what the evidence supports-including when a result is null, unstable, or less useful than a simple baseline.\nYour work may combine structured data, statistical learning, probabilistic modeling, language models, retrieval, tool use, and explicit agent simulation. You will be expected to choose methods based on the problem and evidence rather than on novelty. A strong result is not merely a lower benchmark score; it is a predictive improvement that remains calibrated, survives honest holdouts, and matters for a real decision.\nYou will work closely with Population Research, Evaluation Research, Simulation Engineering, Product Engineering, Data, and Deployment. Validated methods should become reproducible systems with clear limits, not remain isolated notebooks or research demos.\nWhat you will do\nOwn a high-value research question in forecasting, aggregate behavioral prediction, calibration, conditioning, subgroup decomposition, drift, or agentic prediction.\n\nTurn ambiguous questions into falsifiable hypotheses, strong baselines, appropriate datasets, decisive experiments, and explicit criteria for success or stopping.\n\nBuild prediction methods from real-world records such as transactions, product usage, event histories, operational data, market data, surveys, customer data, and longitudinal outcomes.\n\nCombine language models with structured data, retrieval, tools, quantitative models, and inference-time reasoning when the combination produces measurable value.\n\nDevelop estimates of population behavior and determine how those estimates vary with attributes, prior behavior, information exposure, environment, time, and intervention.\n\nDesign temporal holdouts and prospective tests that use only information available at the time a prediction would actually have been made.\n\nMeasure calibration, proper scoring, ranking quality, selective prediction, subgroup performance, robustness, and the decision cost of different errors.\n\nCompare complex methods with strong alternatives, including historical rates, conventional statistical models, direct aggregate prediction, segment-level methods, and explicit population simulation.\n\nDiagnose error by population, subgroup, time period, domain, data source, confidence level, and model component rather than relying only on an average metric.\n\nStudy when agent-based reasoning adds predictive signal and when it merely adds cost, variance, or persuasive explanations.\n\nBuild learning loops from resolved events and customer outcomes while protecting final evaluation sets from contamination.\n\nWork with Population Research to determine whether a failed prediction originates in the representation of the population or in the prediction method.\n\nWork with Evaluation Research to create measurements that are both diagnostic during development and credible enough for final claims.\n\nHand validated methods to Simulation Engineering with reproducible code, documented assumptions, known failure modes, and clear production requirements.\n\nWrite research notes and technical reports that make the question, method, data, uncertainty, result, and limitations understandable to others.\n\nCommunicate negative and inconclusive results plainly and redirect quickly when the evidence does not justify further investment.\n\nRepresentative research directions\nYou might investigate questions such as:\nForecast a future business, market, policy, or behavioral outcome using only information that was available when the forecast would have been issued.\n\nPredict demand, adoption, conversion, retention, purchasing behavior, message response, or another aggregate outcome from transaction and usage data.\n\nDevelop decomposition-based methods that predict a population marginal through meaningful subgroups without amplifying sparse-data error.\n\nEstimate how an outcome should update after a price change, product launch, information event, policy change, or shift in the economic environment.\n\nModel marginal drift over time as new information arrives and determine when a prior prediction should be revised, widened, or withdrawn.\n\nBuild an agentic forecasting system that retrieves evidence, decomposes a question, tests assumptions, compares hypotheses, and returns a calibrated estimate.\n\nCompare direct prediction with population-based simulation to identify when representing individual agents creates a real gain.\n\nDevelop methods for rare, novel, or rapidly changing settings where labels are sparse and conventional supervised learning is unreliable.\n\nStudy how prediction quality changes with model capability, retrieval quality, inference-time computation, data coverage, historical context, and population representation.\n\nImprove selective prediction so the system can identify cases where the available evidence does not support a confident claim.\n\nHow we work\nWe measure progress against future or otherwise held-out outcomes. Calibration, temporal validity, subgroup behavior, selection effects, leakage, and performance under shift are first-class research concerns. A simple baseline that wins is more valuable than a sophisticated method that cannot demonstrate signal.\nExploration is encouraged, but experiments should be designed to fail clearly. We prefer a well-supported null that changes our beliefs over an ambiguous improvement that survives only through favorable choices. Research should eventually change what Aaru can build or what the company believes.\nResearchers own their work from question selection through implementation, evaluation, explanation, and production handoff. Collaboration is intensive, but responsibility for reaching a truthful conclusion is not diffuse.\nYou might thrive in this role if\nYou have a record of rigorous work in machine learning, forecasting, statistics, econometrics, quantitative research, decision science, or a related field.\n\nYou have built predictive systems from messy, heterogeneous data and evaluated them against observed outcomes.\n\nYou are comfortable moving between statistical reasoning, model design, data construction, implementation, experiment design, and detailed error analysis.\n\nYou understand calibration and can explain why accuracy alone is often insufficient for consequential prediction.\n\nYou know how temporal leakage, selection effects, label construction, dataset shift, and subgroup imbalance can create convincing but invalid results.\n\nYou understand the strengths and failure modes of language models and are willing to combine them with-or replace them by-simpler quantitative methods.\n\nYou generate your own questions, prioritize the uncertainty that matters most, and design experiments that can resolve it.\n\nYou write strong research code and are willing to improve it enough that another team can reproduce and productionize the result.\n\nYou communicate clearly, including when the evidence is negative, fragile, or inconsistent with your preferred hypothesis.\n\nYou want to work in person in New York with a team that moves quickly and treats empirical truth as the standard.\n\nStrong candidates may also have\nWork in time-series modeling, econometrics, recommender systems, demand forecasting, risk modeling, causal inference, quantitative social science, or probabilistic programming.\n\nExperience with LLM agents, retrieval, tool use, post-training, synthetic environments, model-based reasoning, or inference-time scaling.\n\nExperience with prospective forecasting, prediction markets, decision-making under uncertainty, or operational prediction systems.\n\nExperience building proprietary datasets, joining noisy external sources, or constructing outcomes and features with careful temporal provenance.\n\nA publication record or open research demonstrating original empirical work, though shipped or operational results are equally valuable.\n\nExperience with behavioral, transaction, product, market, polling, or longitudinal data.\n\nExperience taking a research method through production deployment and learning from how it behaved in the field.\n\nCandidates need not have\nPrior experience in population simulation or a background exclusively in language-model research.\n\nA PhD, provided you can demonstrate equivalent research depth and empirical rigor.\n\nEvery method listed above. We care most about your ability to choose the right method, test it honestly, and learn quickly.\n\nWhat success looks like\nYou own an important prediction problem and create a clear body of evidence about what works, what does not, and under which conditions.\n\nYour methods outperform strong baselines on clean historical and prospective outcomes rather than only on development benchmarks.\n\nImprovements are calibrated, robust across relevant groups and time periods, and honest about cases where the evidence is weak.\n\nYour work clarifies whether errors originate in population representation, conditioning information, data quality, model choice, or evaluation design.\n\nResearch artifacts are reproducible, well documented, and straightforward for collaborators to inspect and challenge.\n\nValidated methods move into production and improve the quality of Aaru's simulations or customer-facing decisions.\n\nNegative results and failed approaches reduce uncertainty and prevent the company from investing in attractive but unsupported ideas.\n\nOther researchers trust your judgment because you combine technical ambition with unusually strong empirical discipline.\n\nLocation and benefits\nThis role is based in New York City. Aaru is an in-person company, working five days a week in the office. Candidates should be located in the New York metropolitan area or open to relocation.\nAaru offers a competitive...","description_format":"text","description_chars":12249,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"phd","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Predictive Analytics","Market Research"],"lifecycle":[{"event":"open","at":"2026-09-10T21:38:19Z"}],"visa":[],"liveness":{"score":4,"band":"cold","label":"Long shot","p_open":1,"p_active":0.138,"p_room":0.28,"age_days":70,"expected_fill_days":10,"reasons":["conf:1","win:tail","crowd:"],"computed_at":"2026-11-01T06:10:25Z"},"pay":{"stated_usd_annual":600000,"is_top_pay":true},"html_url":"https://alion.io/job/aaru-prediction-researcher","json_url":"https://alion.io/job/aaru-prediction-researcher.json","meta":{"generated_at":"2026-11-01T20:08:18Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":30958,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-02T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":668926},"rest":"https://alion.io/mcp/rest/get_company?id=668926"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Faaru-prediction-researcher"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Faaru-prediction-researcher"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Faaru-prediction-researcher"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/aaru-prediction-researcher\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Faaru-prediction-researcher"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":661860}},{"tool":"search_jobs","arguments":{"company":"aaru.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":661860},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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