Lead Data Scientist

Job Description:

The Wall Street Journal is seeking a Lead Data Scientist to drive our newsroom's audience-data strategy. Reporting to the Senior Manager, Newsroom Data, you will be embedded in one of the world's most influential newsrooms, working directly with newsroom coverage, audience, and product strategy teams on the decisions that shape how news is reported, prioritized, and delivered to millions of readers. 

This is a highly autonomous and strategic role that demands both rigorous data science and sharp analytical instincts in equal measure. Working closely with editorial and audience leaders, you'll surface the right problems and own the full arc from solution design to adoption, building the metrics, models, and forecasts that teams rely on. This includes developing tools teams actually use, translating complex methodologies into clear recommendations, and consistently connecting your work to outcomes that matter: how readers discover, engage with, and return to WSJ journalism. Beyond your own output, you will serve as a technical anchor for the team, mentoring colleagues, establishing shared standards for analytical rigor, and proactively driving continuous improvements across our workflows. The ideal candidate seamlessly balances statistical rigor, strategic newsroom thinking, and a genuine investment in the people and practices around them.

This position will be based in our New York office.

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.

You have:

• 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.