Lead Data Scientist
About TEGNA TEGNA Inc. (NYSE: TGNA) helps people thrive in their local communities by providing the trusted local news and services that matter most. With 64 television stations in 51 U.S. markets, TEGNA reaches more than 100 million people monthly across the web, mobile apps, streaming, and linear television. Together, we are building a sustainable future for local news. We are seeking a Lead Data Scientist to drive the development and deployment of predictive models for our Sales Automation initiative. You will own the end-to-end data science lifecycle to build machine learning models that predict advertiser probability of conversion to customer, enabling our sales team to prioritize high-value prospects, optimize outreach strategies, and accelerate revenue growth. Ideal candidates are analytical problem-solvers who excel at translating business objectives into data-driven solutions, can work with complex advertiser datasets, and thrive in a fast-paced, collaborative environment. Responsibilities: What You'll Do Own the full data science lifecycle for advertiser conversion modeling: problem framing, hypothesis design, feature engineering, model development, validation, deployment, and impact measurement. Translate complex analytics and machine learning outputs into consumable business insights by developing Tableau- or Power BI–like interactive dashboards and generating automated BI reports (Word, PDF, PowerPoint) that support executive decision-making and revenue strategy. Build and optimize predictive models to estimate advertiser probability of conversion using historical sales data, advertiser behavior signals, engagement metrics, and market trends. Leverage managed machine learning and predictive modeling capabilities through cloud platforms (e.g., Snowflake Cortex ML, Snowpark ML) to rapidly prototype, rigorously evaluate, and productionize advertiser conversion probability models, ensuring scalability, reliability, and alignment with sales and revenue business use cases. Engineer robust features from multi-source advertiser datasets including firmographics, engagement history, interaction patterns, and campaign performance; handle data quality issues, missing values, and class imbalance. Develop classification models (logistic regression, gradient boosting, neural networks, ensemble methods) with strong emphasis on interpretability and business explainability for sales team adoption. Design and implement model validation frameworks: train/test splits, cross-validation, business-aligned metrics (precision, recall, AUC, lift), and rigorous backtesting against historical conversion data. Establish model governance and monitoring: performance tracking, drift detection, retraining pipelines, fairness assessment, and clear documentation of model assumptions and limitations. Create actionable conversion propensity scores and segmentation strategies that enable sales teams to prioritize leads, personalize outreach, and optimize resource allocation. Conduct A/B testing and incrementality analysis to measure the business impact of model-driven sales interventions and continuously improve conversion strategies. Translate complex model outputs into clear, executive-ready narratives and dashboards; deliver recommendations that drive sales strategy and revenue decisions. Partner with sales, marketing, and product teams to understand business requirements, prioritize high-impact opportunities, and design data-informed roadmaps. Mentor teammates on best practices in predictive modeling, statistical rigor, and responsible AI; promote a culture of experimentation and measurable impact. Core Skills and Methods Predictive modeling and classification: logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), neural networks, and ensemble methods. Data Visualization: Create clear, compelling data visualizations and dashboards to enable team-led exploratory analysis and effectively communicate to both technical and non-technical stakeholders Feature engineering: domain-driven feature creation, feature selection, handling categorical variables, scaling, and dimensionality reduction. Statistical methods: hypothesis testing, confidence intervals, statistical significance testing, and understanding of Type I/II errors. Imbalanced classification: techniques for handling class imbalance (SMOTE, class weights, threshold optimization) and appropriate evaluation metrics. Model evaluation and validation: cross-validation, ROC/AUC analysis, precision-recall curves, calibration, and business-aligned performance metrics. Time series and temporal analysis: handling temporal patterns in conversion data, seasonality effects, and recency bias. Experimentation and causal inference: A/B testing, power analysis, propensity score matching, and measuring true incremental impact. Data wrangling and SQL: extracting, transforming, and aggregating complex advertiser and sales datasets from multiple sources. Model deployment and monitoring: model versioning, performance tracking, automated retraining, and production monitoring. Communication and storytelling: translating model insights into clear business recommendations and actionable strategies for non-technical stakeholders. Requirements: What Makes You a Great Fit 5+ years of applied data science experience with proven track record building and deploying predictive models in production environments. Demonstrated expertise in classification modeling and conversion/propensity prediction (e.g., customer acquisition, churn prediction, lead scoring). Hands-on experience with Snowflake - Snowpark (Python) and/or Snowflake ML / Cortex ML Advanced SQL skills for large-scale analytical datasets Experience with feature engineering on structured business data Strong proficiency in Python (scikit-learn, pandas, numpy) and SQL for data extraction and feature engineering. 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 ability to work cross-functionally with sales, marketing, and product teams. Commitment to responsible modeling: transparency, fairness, and clearly communicated limitations and risks. Excellent communication skills with ability to explain complex models to non-technical stakeholders. Preferred Advanced degree in Data Science, Statistics, Machine Learning, or related field. Prior experience in sales, marketing, or revenue analytics domains. Experience with cloud platforms (AWS, GCP, Azure) and big data tools. Familiarity with MLOps practices and model deployment pipelines. ### Benefits: TEGNA offers comprehensive benefits designed to safeguard the physical, mental and financial health of our employees and their families. TEGNA offers two medical plan options for full and part-time employees through Blue Cross Blue Shield of Texas, as well as access to dental and eye care coverage; fertility, surrogacy and adoption assistance; disability and life insurance. Our 401(k) program offers full, part-time and temporary employees the opportunity to contribute 1% - 80% of their pay on a pre-tax basis to TEGNA's 401(k). Contributions made up to the first 4% of pay are eligible for a 100% match from the company and are 100% vested from day one. Regardless of participation in TEGNA medical plans, ALL employees and their eligible family members receive nine free virtual doctor's appointments with a physician through Teladoc, and 12 free annual therapy sessions with a licensed clinician through Spring Health. TEGNA offers a generous Paid Time Off (PTO) benefit as well as nine paid holidays per year. Some jobs are covered by a collective bargaining agreement and thus some or all of the benefits described herein may not apply. For example, some newsroom bargaining unit employees receive health and retirement benefits under plans administered by the union. EEO statement: TEGNA Inc. is a proud equal opportunity employer. We are proud to be an equal opportunity employer, hiring and developing individuals from diverse backgrounds and experiences to add to our collaborative culture. We value and consider applications from all qualified candidates without regard to actual or perceived race, color, religion, national origin, sex, gender, age, marital status, personal appearance, sexual orientation, gender identity or expression, family responsibilities, disability, medical condition, enrollment in college or vocational school, political affiliation, military or veteran status, citizenship status, genetic information, or any other basis protected by federal, state or local law. TEGNA will reasonably accommodate qualified individuals with disabilities in accordance with applicable law. If you are in need of an accommodation in order to submit your application, please email askhr@tegna.com Recruiting Fraud Alert: To all candidates: your personal information and online safety are important to us. Only TEGNA Recruiters or Hiring Managers will reach out to you regarding consideration of your application or background. 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