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About This Role
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.
To apply, please submit a resume and a cover letter explaining how your skills, experience and interests align with the expectations of the role by July 28th. Applications will be reviewed on a rolling basis, and we encourage early submission as the position may be filled before the deadline.
The Journal’s reporters, editors, developers, and audio and visual journalists create important and impactful stories, firmly rooted in fact and adhering to the highest ethical standards. We report without fear or bias, and we maintain a proper sense of perspective, detachment and objectivity in our reporting.
- LI\-JA1\-WSJ
Reasonable accommodation: Dow Jones, Making Careers Newsworthy \- All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, protected veteran status, or disability status. EEO/AA/M/F/Disabled/Vets. Dow Jones is committed to providing reasonable accommodation for qualified individuals with disabilities, in our job application and/or interview process. If you need assistance or accommodation in completing your application, due to a disability, email us at [email protected]. Please put "Reasonable Accommodation" in the subject line and provide a brief description of the type of assistance you need. This inbox will not be monitored for application status updates.
Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, age, national origin, protected veteran status, disability status or any other protected characteristic under applicable law. EEO/Disabled/Vets
Reasonable Accommodation
We are committed to providing reasonable accommodation for qualified individuals with disabilities in our job application and/or interview process. If you need assistance or accommodation in completing your application or participating in an interview due to a disability, email us at [email protected]. Please put "Reasonable Accommodation" in the subject line and provide a brief description of the type of assistance you need. This inbox will not be monitored for application status updates.
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Business Area: Dow Jones \- News \- WSJ
Job Category: Data Analytics/Warehousing \& Business Intelligence
Union Status:
Union role
Base Pay Range: $135,000 \- $155,000
We’re committed to offering competitive and flexible compensation to attract top talent. This pay range reflects our good faith estimate for the role and may vary based on a candidate’s experience, skills, location, and other relevant factors.
For bonus\-eligible roles, targets are determined based on multiple considerations, including market benchmarks and individual contributions.
For benefits\-eligible roles, we offer a comprehensive and competitive benefits package covering health, retirement, wellbeing, and more, along with optional benefits to meet the diverse needs of our employees.
The Wall Street Journal is a global news organization that provides leading news, information, commentary and analysis. The Wall Street Journal engages readers across print, digital, mobile, social, audio, and video. Building on its heritage as the preeminent source of global business and financial news, the Journal includes coverage of U.S. and world news, politics, arts, culture, lifestyle, sports, and health. It holds more than three dozen Pulitzer Prizes for outstanding journalism. The Wall Street Journal is published by Dow Jones, a division of News Corp (NASDAQ: NWS, NWSA; ASX: NWS, NWSLV).
Req ID: 53819
Salary Context
This $135K-$155K range is below the median for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At News Corp, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($145K) sits 25% below the category median. Disclosed range: $135K to $155K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
News Corp AI Hiring
News Corp has 12 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager. Positions span New York, NY, US, Austin, TX, US. Compensation range: $95K - $270K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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