TEGNA seeks to transform rich viewership, content, and sales datasets into predictive models, optimization frameworks, and actionable insights that shape content strategy, audience growth, and advertising performance within a multi-platform media ecosystem.
Requirements
- Statistical modeling and inference: generalized linear models, hierarchical/mixed effects, survival/retention analysis, causal inference (matching, diff-in-diff, instrumental variables where appropriate).
- Forecasting and demand modeling: univariate/multivariate time series, seasonality and event effects, hierarchical reconciliation, anomaly detection, and nowcasting.
- Machine learning for structured data: classification, regression, ranking, uplift modeling, and ensemble methods; strong emphasis on feature engineering and interpretability.
- Natural language processing for media: text normalization, embeddings, topic modeling, entity and key-phrase extraction, similarity search, content clustering, and evaluation of relevance/quality.
- Experimentation and measurement: A/B and multivariate testing, power analysis, sequential testing safeguards, uplift/causal experimentation, incrementally for ads and content.
- Optimization and decisioning: inventory and pricing optimization, budget allocation, scheduling/lineup optimization, and portfolio trade-off analysis.
- Data acumen: wrangling complex, high-volume, multi-platform datasets; designing reliable labels and ground truth; handling sparsity, delayed feedback, and feedback loops.
Responsibilities
- Own the full data science lifecycle: problem framing, hypothesis design, feature strategy, model development, validation, deployment planning, and impact measurement.
- Build audience and engagement models using multi-source viewership and behavioral signals (e.g., forecasting, churn/retention, cross-platform attribution, time-series and panel-based analysis).
- Develop content intelligence with NLP: taxonomy/labeling, semantic similarity, topic and entity modeling, summarization, quality/relevance scoring, content-to-audience matching, and trend detection.
- Create sales and advertising analytics: demand forecasting, pricing/revenue optimization, propensity and uplift modeling, inventory allocation, and campaign effectiveness measurement.
- Design interpretable measurement frameworks: incrementality tests, controlled experiments, uplift/A/B testing, holdouts, and causal inference for content and ads.
- Unify cross-platform metrics to produce holistic funnels and KPIs; define leading indicators and build early-warning/nowcasting approaches for performance health.
- Engineer robust features from logs, sessions, sequences, and text; handle seasonality, cold start, sparsity, and platform-specific biases.
Other
- 5+ years of applied data science experience delivering measurable impact in consumer, media, advertising, or adjacent domains.
- Proven track record building models from viewership, content, and sales datasets, with shipped or adopted solutions that informed programming, growth, or revenue.
- Comfort navigating ambiguity, quickly forming hypotheses, and iterating toward practical, high-signal solutions.
- Balanced mindset across rigor and speed: you know when to prototype, when to harden, and how to quantify trade-offs.
- Strong collaboration skills and curiosity about how editorial, product, growth, and sales teams operate and make decisions.
