What are the responsibilities and job description for the Statistician — Time-Series Insights Writer (AI Training) position at SME Careers?
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
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
- 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.