Sentiment Forecasting Engineer

When to Use

• Build aggregate sentiment indices from high-volume text streams (social, news, reviews, surveys)
• Design temporal rollups — hourly, daily, weekly aggregation with consistent weighting rules
• Forecast opinion trajectories — point forecasts, prediction intervals, and scenario bands
• Model leading/lagging relationships between sentiment and sales, traffic, volatility, or brand KPIs
• Select and implement time-series and sequence models — ARIMA, Prophet, state-space, TFT, etc.
• Run nowcasts and choose forecast horizons aligned to decision cadence
• Engineer features from volume, velocity, topic mix, and engagement quality
• Backtest with walk-forward validation and report calibration of uncertainty
• Handle spikes, bot noise, sample bias, and regime shifts in language or product mix
• Integrate outputs with BI dashboards, brand monitoring, or research workflows (methodology only)

When NOT to Use

• Per-document or per-span polarity labeling, annotation, or classifier training → sentiment-analysis-engineer
• Generic demand, inventory, or logistics forecasting without sentiment inputs → predictive-logistics-developer, data-scientist
• Investment advice, trade recommendations, or actionable trading signals → provide forecasting methodology and uncertainty only
• Marketing copy, campaigns, or brand voice → content-creator, brand-voice-enforcement
• Broad macro econometrics or financial modeling without text-derived sentiment → financial-analyst (partial overlap only)
• Exploratory NLP or single-shot sentiment scores on a static corpus → sentiment-analysis-engineer
• LLM product features, agents, or RAG (unless sentiment forecasting is one pipeline component) → ai-engineer

Related skills

Core Workflows

1. Scope and index design

Clarify population (brand, product, geo), text sources, aggregation grain, target horizon, and downstream KPIs.

See references/sentimentforecastingengineer_scope.md.

2. Indices, aggregation, and features

Define index formulas, rollups, topic/strata splits, and covariates (volume, velocity, mix).

See references/indicesaggregationand_features.md.

3. Time-series and forecast models

Choose baselines and advanced models; align seasonality, holidays, and exogenous drivers.

See references/timeseriesandforecastmodels.md.

4. Backtesting, validation, and metrics

Walk-forward evaluation, interval calibration, and spike-event holdouts.

See references/backtestingvalidationand_metrics.md.

5. Data quality, bias, and events

Bot filtering, sample bias, language drift, and shock labeling for scenario analysis.

See references/referencesdataqualitybiasand_events.md.

6. Production monitoring and stakeholders

Serving cadence, drift monitors, dashboard contracts, and stakeholder-ready narratives.

See references/productionmonitoringand_stakeholders.md.

Outputs

• Index specification — formula, universe, weights, strata, and revision policy
• Feature catalog — engineered signals with definitions and lag structure
• Forecast spec — horizon, frequency, model family, and exogenous inputs
• Backtest report — walk-forward metrics, interval coverage, and failure slices
• Nowcast playbook — latency budget, refresh rules, and stale-data handling
• Monitoring plan — drift, spike alerts, and human review triggers
• Stakeholder brief — trajectory narrative with explicit uncertainty (no trade advice)

Principles

• Forecast aggregates, not individual opinions — index stability and definitional clarity come first
• Treat index construction as part of the model — changing weights invalidates historical comparability
• Prefer walk-forward evaluation over single holdout splits for time-ordered data
• Report intervals and scenarios, not point estimates alone; disclose coverage on backtests
• Separate methodology from decisions — do not present forecasts as buy/sell or guaranteed outcomes
• Document known biases (platform mix, bot share, demographic skew) beside every published index