Staff Data Analyst, Analytics Capabilities

The Washington Post•Washington, DC

41d•Onsite

About The Position

We're on a mission to deliver riveting storytelling for all of America. At The Washington Post, you'll help reinvent news. Our work is driven by a deep investigative spirit and enhanced by innovation to bring audiences closer to the stories that matter most. The DAI (Data, Analytics & Insights) department drives strategic and operational decisions for The Washington Post.â¯ This highly visible team applies data, analytics, and research to support strategic initiatives and day-to-day operational support company wide. As our Staff Data Analyst, you'll lead data initiatives to understand and unlock actionable insights that drive audience growth, engagement, retention, and monetization. Your insights will be instrumental in fostering long-term customer loyalty and maximizing recurring revenue. You'll work in close collaboration with the newsroom, product, marketing, engineering, advertising and finance departments on a diverse range of projects that have a critical and strategic impact on all aspects of the company. You will regularly interact with various stakeholders in these departments, understand needs, build reports, conduct data analysis, and make actionable recommendations. You will love solving open-ended problems by taking mountains of data, transforming it into meaningful analysis aimed at identifying solutions and opportunities.

Requirements

Education: Bachelor's or master's in data science, Statistics, Business Analytics, or related field.
Experience: 6+ years of experience in data analytics, with significant focus on customer and content in subscription businesses.
Skills & Expertise:
Expertise in SQL, Python, and data visualization tools (e.g., Tableau, Power BI).
Expertise in relational Database Systems, Advanced SQL and Microsoft Excel. Experience with MySQL on cloud or AWS Redshift.
Experience with data science and AI frameworks such as TensorFlow, PyTorch, or Hugging Face.
Strong ability to translate AI outputs into business-relevant insights and strategic recommendations.
Excellent communication and collaboration skills across technical and non-technical stakeholders.

Nice To Haves

Experience in media, publishing, streaming, ecommerce, or content-driven businesses.
Experience operationalizing AI models within business intelligence tools or self-service platforms.
Proven track record of developing or scaling AI-powered agents or insight generation systems.
Familiarity with LLMs, retrieval-augmented generation (RAG), and generative AI applications in analytics.
Knowledge of data governance, privacy, and ethical considerations when deploying AI in enterprise environments.
Experience mentoring and developing other analysts.

Responsibilities

Drive deep analysis of audience behavior across platforms (web, app, newsletters, video, social, etc).
Develop and refine customer segmentation frameworks to inform content strategy and personalization.
Analyze customer journeys to optimize acquisition, onboarding, engagement, retention, and reactivation.
Build lifetime value (LTV) models and customer propensity models.
Develop and maintain content performance dashboards and reporting to surface actionable insights.
Analyze content consumption patterns to inform editorial strategy, content formats, and programming decisions.
Partner with editorial, product, and marketing teams to test and optimize content strategies.
Identify trends in audience preferences and emerging content opportunities.
Serve as a trusted thought partner to leaders across Content, Product, Marketing, and Revenue teams.
Translate complex analytical findings into clear business recommendations.
Present insights and influence senior leadership on customer and content strategy.
Mentor and coach junior analysts on advanced techniques and best practices.
Help define and measure key KPIs for audience engagement, content success, and customer value.
Advocate for improvements in data collection, tagging, and experimentation frameworks.
Partner with Data Engineering and Product teams to ensure high data quality and instrumentation coverage.
Explore and test new analytical approaches (e.g., causal inference, behavioral clustering, NLP for content tagging).
Leverage tools like SQL, Python, R, Tableau, and other BI platforms.