Director, Data Science

Dotdash Meredith

5d•$175,000 - $210,000•Remote

About The Position

People Inc. is America’s largest print and digital publisher, reaching tens of millions of readers every day. We’re hiring a Director of Data Science to lead customer intelligence and personalization for our entertainment brands, helping shape products and experiences that inform, engage, and inspire our audiences. In this role, you’ll build a deeper understanding of our customers across product engagement, marketing response, and lifecycle behavior. You’ll partner closely with Product, Marketing, and Editorial teams to turn data into actionable insights that drive growth, while working with Data Operations and Engineering to ensure our models, measurement systems, and insights can scale effectively. You’ll apply statistical modeling and machine learning to product and marketing challenges across customer segmentation, acquisition, engagement, retention, and lifetime value, with a particular focus on mobile app experiences. You’ll own models and analyses that support personalization, automated marketing, attribution, experimentation, and lifecycle optimization across apps, newsletters, and other customer touchpoints, translating complex audience behavior into clear recommendations and measurable business impact.

Requirements

PhD or advanced degree in Data Science, Economics, Business Analytics, Mathematics, Statistics, or similar quantitative field, or equivalent experience.
7+ years of experience in data science and machine learning with a strong focus in the area of customer behavior, product analytics, and marketing-focused problems.
5+ years of experience leading a team of data scientists.
Ability to lead product and funnel analytics on web and mobile platforms with a focus on providing actionable insights, not just data outputs.
Solid foundation in causal inference, survival analysis, and time series analysis, especially as applied to marketing and customer behavior.
Strong proficiency in Python and SQL for data analysis and model development.
Familiarity with Claude Code/Codex and how they can be used reliably (and where they fail) in a data science workflow.
Strong strategic and operational capabilities.
Excellent leadership and communication skills.
Ability to communicate complex analyses clearly to non-technical marketing and business stakeholders.
Drives adoption of new data science methods and technologies to continually expand what the product can do.

Nice To Haves

Experience with large-scale search and recommendation systems in media, e-commerce, or advertising space.
Experience working with large-scale analytics platforms (e.g., BigQuery).
Exposure to reinforcement learning or bandit approaches for personalization or offer optimization.
Advanced experimentation techniques, including multivariate testing, factorial designs, and CUPED.
Experience with Bayesian statistical methods for inference and decision-making under uncertainty.
Experience with forecasting models and advanced lifecycle or retention modeling.
Applied NLP experience for content classification, semantic search, or audience understanding.
Track record of leading end-end data science projects from problem framing through deployment and impact measurement.
Confidence and pride in representing Data Science as a strategic partner to Marketing and other business teams.

Responsibilities

Lead and grow a data science team to develop predictive models that estimate customer lifetime value using advanced statistical and machine learning techniques.
Analyze and understand customer interactions across app, web, social media, and email touchpoints to identify and optimize engagement opportunities.
Own product funnel and engagement analysis, including onboarding completion, feature adoption, feature stickiness, and session behavior, as distinct from marketing-funnel metrics such as acquisition and retention.
Leverage data-driven insights to build customer segmentation models, identifying high-value users and informing content and product recommendations.
Partner with Product and Editorial teams to translate user understanding into differentiated experiences across the app, newsletters, and other customer interactions, feeding insights directly into the product development cycle. This includes the sizing of opportunities and assessment of product success.
Design online A/B testing experiments to measure the impact of product changes, personalization, and full-funnel marketing interventions.
Ensure effective collaboration between data science teams and other departments.
Advocate for resources and tools to advance data capabilities.
Align data initiatives with company objectives.