# Lead Data Scientist, Newsroom Analytics

**Company**: The Wall Street Journal
**Location**: NYC - 1211 Ave of the Americas
**Work arrangement**: onsite
**Experience**: senior
**Job type**: full-time
**Salary**: $135,000 - $155,000
**Category**: Data Science
**Industry**: Media & Entertainment

**Apply**: https://dowjones.wd1.myworkdayjobs.com/en-US/Dow_Jones_Career/job/NYC---1211-Ave-of-the-Americas/Lead-Data-Scientist--Newsroom-Analytics_Job_Req_53819?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_f1d7d500-892

## 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

## Skills

### Required
- Python
- SQL
- Airflow
- Git
- Snowflake
- BigQuery
- dbt
- causal inference
- statistical methods

### Nice to have
- NLP
- text analysis
- topic modeling
- classification
- entity extraction
- off-platform attribution
- audience measurement
- AI

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