Remote Statistician — Time-Series Insights Writer (AI Training) - AI Trainer
SuperAnnotate, Tyler, TX, United States
We’re seeking Statisticians with strong English writing skills to join our Deep Research for Forecasting project. In this role, you will review time-series plots (a quantity of interest over time) along with brief contextual descriptions, then identify the most meaningful patterns in the data and produce concise, well-reasoned causal chains explaining what likely drove those patterns.
Your work will help create high-quality tasks used to train and evaluate AI systems on forecasting-related reasoning. You will focus on distinguishing signal from noise, articulating plausible mechanisms (root cause → intermediate drivers → observed time-series impact), and writing explanations that are clear, grounded, and useful for downstream model training.
Key Responsibilities
Create Forecasting Training Tasks: Given a time-series plot and short description, identify the most important patterns (trend, seasonality, regime changes, outliers, step changes, cyclical behavior, variance shifts) and document them clearly.
Write Causal Chains: Produce concise causal narratives that explain patterns from root cause → mechanism → observable time-series effect, prioritizing the most meaningful drivers and avoiding generic explanations.
Ensure Clarity & Usefulness for AI Training: Write structured, high-signal explanations that are easy to evaluate, minimizing ambiguity and making assumptions explicit when necessary.
Maintain Consistency & Quality: Follow project guidelines and rubrics to ensure outputs are accurate, coherent, and comparable across many examples.
Weekly Commitment: 10 hours / week
Your Profile
You have an educational and/or professional background in Statistics or a closely related field (e.g., Mathematics, Data Science).
Proficient in time-series analysis and forecasting (e.g., trend / seasonality, structural breaks, anomalies, volatility shifts, lag effects).
Excellent English writing skills with a clear, structured, concise style.
Strong analytical judgment and ability to interpret data visualizations with precision.
Comfortable forming plausible causal explanations while clearly separating evidence from assumptions.
Optional: Domain knowledge in one or more of the following: Healthcare; Climate / oceanography; Economics & finance; Cloud operations; Transportation.
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Your work will help create high-quality tasks used to train and evaluate AI systems on forecasting-related reasoning. You will focus on distinguishing signal from noise, articulating plausible mechanisms (root cause → intermediate drivers → observed time-series impact), and writing explanations that are clear, grounded, and useful for downstream model training.
Key Responsibilities
Create Forecasting Training Tasks: Given a time-series plot and short description, identify the most important patterns (trend, seasonality, regime changes, outliers, step changes, cyclical behavior, variance shifts) and document them clearly.
Write Causal Chains: Produce concise causal narratives that explain patterns from root cause → mechanism → observable time-series effect, prioritizing the most meaningful drivers and avoiding generic explanations.
Ensure Clarity & Usefulness for AI Training: Write structured, high-signal explanations that are easy to evaluate, minimizing ambiguity and making assumptions explicit when necessary.
Maintain Consistency & Quality: Follow project guidelines and rubrics to ensure outputs are accurate, coherent, and comparable across many examples.
Weekly Commitment: 10 hours / week
Your Profile
You have an educational and/or professional background in Statistics or a closely related field (e.g., Mathematics, Data Science).
Proficient in time-series analysis and forecasting (e.g., trend / seasonality, structural breaks, anomalies, volatility shifts, lag effects).
Excellent English writing skills with a clear, structured, concise style.
Strong analytical judgment and ability to interpret data visualizations with precision.
Comfortable forming plausible causal explanations while clearly separating evidence from assumptions.
Optional: Domain knowledge in one or more of the following: Healthcare; Climate / oceanography; Economics & finance; Cloud operations; Transportation.
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