Description
The Wall Street Journal is seeking a Lead Data Scientist, Newsroom Analytics to drive our newsroom's audience-data strategy.
This is a highly autonomous and strategic role that demands both rigorous data science and sharp analytical instincts in equal measure.
You will:
- Partner with our digital strategy, coverage and product teams to identify the highest-impact analytical opportunities, define the right questions, and shape the data science roadmap around problems that drive real newsroom outcomes.
- Design and build predictive and explanatory models end-to-end, from feature engineering and validation through production, making principled tradeoffs between complexity and interpretability along the way.
- Translate quantitative findings into clear, actionable recommendations for senior newsroom and business stakeholders, and partner with cross-functional teams to see those recommendations through to adoption.
- Own the core metrics and measurement systems that newsroom teams rely on to evaluate performance and make editorial decisions, ensuring they are accurate, well-documented, and trusted.
- Apply rigorous statistical thinking to measure real-world editorial and audience impact, drawing on causal inference and observational methods alongside controlled experimentation to isolate what's actually driving outcomes.
- Mentor and develop junior data team members, establishing shared standards for rigorous, production-ready analysis and building the team's collective technical capability over time.
Requirements:
- 5+ years of experience in data science, analytics, or applied machine learning, preferably in a media, publishing, or subscription-based environment.
- Proven ability to own models end-to-end in a lean team setting, including scheduling, maintaining, and iterating on outputs using orchestration tools such as Airflow.
- Strong proficiency in Python including feature engineering, model training, validation, and interpretation, with experience maintaining production-quality, reproducible code in Git.
- Advanced SQL and hands-on experience with large-scale data warehouses (Snowflake/BigQuery) as well as ETL workflows/analytics engineering frameworks (dbt).
- Experience applying causal inference and statistical methods to measure real-world outcomes from observational data.
- Strong communication skills with the ability to frame quantitative findings as clear business or editorial recommendations for senior non-technical stakeholders, and a demonstrated ability to influence decisions without direct authority.
- Experience building models that influence content strategy, audience development, or consumer retention.
Standout candidates will have strengths in one or more areas of the following:
- Experience with NLP or text analysis methods, including topic modeling, classification, or entity extraction applied to content data.
- Familiarity with off-platform attribution and audience measurement, specifically modeling the relationship between distributed content, platform referrals, and downstream subscription or engagement outcomes.
- Experience leveraging AI to democratize data and enable faster time-to-insight.
Base Pay Range: $135,000 - $155,000