Director data
Job Title
Director Data Modeling, Measurement Innovation Job Description About the Role: Dotdash Meredith is seeking a technically strong Director of Data Modeling to lead advanced analytical initiatives within our Measurement team. This role is central to designing and deploying statistical models—particularly predictive modeling, causal inference, and optimization—leveraging large-scale 1st and 3rd-party datasets to quantify advertising effectiveness across our digital platforms. You will collaborate closely with the Sr. Director of Campaign Measurement and a multidisciplinary team of data scientists, analysts, and engineers. Your work will inform data-driven decision-making and drive scalable measurement frameworks. The ideal candidate brings depth in statistical modeling, hands-on proficiency in Python and SQL, and the ability to interface with both technical and business stakeholders. Hybrid 3x a week- NYC In-office Expectations: This position is hybrid in-office, with the ability to work remotely for up to 2 days per week. Key Responsibilities- Architect, implement, and validate advanced statistical models to measure advertising impact (e.g., propensity models, uplift models, media mix models, etc.).
- Build scalable, modular pipelines to support experimentation (e.g., A/B testing frameworks, holdout designs).
- Apply statistical techniques (e.g., causal inference, Bayesian modeling, hierarchical models) to solve complex measurement challenges.
- Standardize and automate data sourcing, feature engineering, and model monitoring workflows.
- Collaborate with Data Engineering and Data Operations to integrate modeling solutions into production systems (via BigQuery, GCP, etc.).
- Update, tune, and retrain machine learning models for ongoing campaigns and post-campaign analytics.
- Support internal experimentation strategy, including design of experiments (DoE), test/control segmentation, and power analysis.
- Communicate technical methodologies and insights to internal stakeholders and external clients, both technical and non-technical.
- Lead the analytical component of pilot projects and transition successful models into enterprise-wide deployment.
- Visualize model outputs and KPIs using modern BI tools and statistical graphics.
- 8+ years of hands-on experience in predictive analytics, statistical modeling, or machine learning, preferably in digital media or advertising.
- Strong statistical programming in Python (Pandas, scikit-learn, statsmodels, etc.).
- Advanced SQL skills and experience querying large-scale datasets (preferably in BigQuery, Snowflake, or similar).
- Experience designing and interpreting controlled experiments (A/B tests, multi-cell tests).
- Background in integrating disparate data sources for unified modeling pipelines.
- Deep understanding of causal inference methodologies and model evaluation metrics.
- Familiarity with cloud infrastructure (GCP, AWS) for model deployment and data orchestration.
- Prior exposure to adtech stacks, including Google Ad Manager and Google Analytics.
- Strong data visualization and communication skills—ability to explain complex technical topics to senior business stakeholders.
- Proven client-facing experience, with an ability to clearly translate data science concepts into actionable business strategies.
- Detail-oriented mindset with excellent project prioritization skills in fast-paced environments.
- Familiarity with healthcare-related measurement vendors such as Crossix, IQVIA, or Symphony Health.
- Experience managing technical team members across distributed teams and guiding modeling strategy across multiple business units.
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