Remote Statistician — Time-Series Insights Writer; AI Training - AI Trainer

Position: Remote Statistician — Time-Series Insights Writer (AI Training) - AI Trainer
Location: St. Thomas

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.